GeVaCT - Genomic Variant Classifier Tool

Dorien Daneels, Isel Grau, Dipankar Sengupta, Maryse Bonduelle, Dewan Md Farid, Didier Croes, Ann Nowé, Sonia Santana-Varela

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

High throughput screening (HTS) techniques, like mendeliome, whole exome and genome screening, are becoming a routine in a clinical diagnostic setting. However, classifying the identified genomic variants as benign or(likely) pathogenic, is still a tedious and time consuming process for the(clinical) geneticist. To facilitate this variant classification process, we have developed GeVaCT, a standalone Java based tool that implements and automatizes a published variant classification scheme for autosomal dominant disorders. GeVaCT currently supports annotated variant files from Alamut Batch (Interactive Biosoftware), with future plans to support input from other variant annotation tools.
The variant classification process currently implemented in GeVaCT is based on a published scheme in the context of cardiac arrhythmias (Hofman et al.,2013). The implemented scheme consists of two phases: pre-processing and variant classification. During pre-processing, the annotated variant file from Alamut Batch is imported and filtered based on the presence of the variant in databases with described variants or a local database, the variant location,the coding effect and the variant allele frequency in an ethnically matched population. The variant classification workflow depends on the type of variant: either missense or nonsense/frame-shift. Each attribute used gets a weighted score that is summed up with the others to come to a first variant classification. This first score is updated based on familial and functional information obtained for the variant-of-interest. The final result is a classification of the variant in one out of five classes ranging from non-pathogenic to pathogenic.
Original languageEnglish
Title of host publicationEuropean Human Genetics Conference 2016
Place of PublicationBarcelona, Spain
PublisherEuropean Journal of Human Genetics
Pages341
Number of pages1
Volume24
EditionE-Supplement 1
ISBN (Electronic)1476-5438
ISBN (Print)1018-4813
Publication statusPublished - 01 Jul 2015

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Databases
Exome
Workflow
Gene Frequency
Cardiac Arrhythmias
Genome
Population

Cite this

Daneels, D., Grau, I., Sengupta, D., Bonduelle, M., Farid, D. M., Croes, D., ... Santana-Varela, S. (2015). GeVaCT - Genomic Variant Classifier Tool. In European Human Genetics Conference 2016 (E-Supplement 1 ed., Vol. 24, pp. 341). [P16.06] Barcelona, Spain: European Journal of Human Genetics.
Daneels, Dorien ; Grau, Isel ; Sengupta, Dipankar ; Bonduelle, Maryse ; Farid, Dewan Md ; Croes, Didier ; Nowé, Ann ; Santana-Varela, Sonia. / GeVaCT - Genomic Variant Classifier Tool. European Human Genetics Conference 2016. Vol. 24 E-Supplement 1. ed. Barcelona, Spain : European Journal of Human Genetics, 2015. pp. 341
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Daneels, D, Grau, I, Sengupta, D, Bonduelle, M, Farid, DM, Croes, D, Nowé, A & Santana-Varela, S 2015, GeVaCT - Genomic Variant Classifier Tool. in European Human Genetics Conference 2016. E-Supplement 1 edn, vol. 24, P16.06, European Journal of Human Genetics, Barcelona, Spain, pp. 341.

GeVaCT - Genomic Variant Classifier Tool. / Daneels, Dorien; Grau, Isel; Sengupta, Dipankar; Bonduelle, Maryse; Farid, Dewan Md; Croes, Didier; Nowé, Ann; Santana-Varela, Sonia.

European Human Genetics Conference 2016. Vol. 24 E-Supplement 1. ed. Barcelona, Spain : European Journal of Human Genetics, 2015. p. 341 P16.06.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - GeVaCT - Genomic Variant Classifier Tool

AU - Daneels, Dorien

AU - Grau, Isel

AU - Sengupta, Dipankar

AU - Bonduelle, Maryse

AU - Farid, Dewan Md

AU - Croes, Didier

AU - Nowé, Ann

AU - Santana-Varela, Sonia

PY - 2015/7/1

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N2 - High throughput screening (HTS) techniques, like mendeliome, whole exome and genome screening, are becoming a routine in a clinical diagnostic setting. However, classifying the identified genomic variants as benign or(likely) pathogenic, is still a tedious and time consuming process for the(clinical) geneticist. To facilitate this variant classification process, we have developed GeVaCT, a standalone Java based tool that implements and automatizes a published variant classification scheme for autosomal dominant disorders. GeVaCT currently supports annotated variant files from Alamut Batch (Interactive Biosoftware), with future plans to support input from other variant annotation tools.The variant classification process currently implemented in GeVaCT is based on a published scheme in the context of cardiac arrhythmias (Hofman et al.,2013). The implemented scheme consists of two phases: pre-processing and variant classification. During pre-processing, the annotated variant file from Alamut Batch is imported and filtered based on the presence of the variant in databases with described variants or a local database, the variant location,the coding effect and the variant allele frequency in an ethnically matched population. The variant classification workflow depends on the type of variant: either missense or nonsense/frame-shift. Each attribute used gets a weighted score that is summed up with the others to come to a first variant classification. This first score is updated based on familial and functional information obtained for the variant-of-interest. The final result is a classification of the variant in one out of five classes ranging from non-pathogenic to pathogenic.

AB - High throughput screening (HTS) techniques, like mendeliome, whole exome and genome screening, are becoming a routine in a clinical diagnostic setting. However, classifying the identified genomic variants as benign or(likely) pathogenic, is still a tedious and time consuming process for the(clinical) geneticist. To facilitate this variant classification process, we have developed GeVaCT, a standalone Java based tool that implements and automatizes a published variant classification scheme for autosomal dominant disorders. GeVaCT currently supports annotated variant files from Alamut Batch (Interactive Biosoftware), with future plans to support input from other variant annotation tools.The variant classification process currently implemented in GeVaCT is based on a published scheme in the context of cardiac arrhythmias (Hofman et al.,2013). The implemented scheme consists of two phases: pre-processing and variant classification. During pre-processing, the annotated variant file from Alamut Batch is imported and filtered based on the presence of the variant in databases with described variants or a local database, the variant location,the coding effect and the variant allele frequency in an ethnically matched population. The variant classification workflow depends on the type of variant: either missense or nonsense/frame-shift. Each attribute used gets a weighted score that is summed up with the others to come to a first variant classification. This first score is updated based on familial and functional information obtained for the variant-of-interest. The final result is a classification of the variant in one out of five classes ranging from non-pathogenic to pathogenic.

M3 - Conference contribution

SN - 1018-4813

VL - 24

SP - 341

BT - European Human Genetics Conference 2016

PB - European Journal of Human Genetics

CY - Barcelona, Spain

ER -

Daneels D, Grau I, Sengupta D, Bonduelle M, Farid DM, Croes D et al. GeVaCT - Genomic Variant Classifier Tool. In European Human Genetics Conference 2016. E-Supplement 1 ed. Vol. 24. Barcelona, Spain: European Journal of Human Genetics. 2015. p. 341. P16.06