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The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments
The Genome Analysis Toolkit (GATK) is a popular set of programs for discovering and genotyping variants from next-generation sequencing data. The current GATK recommendation for RNA sequencing (RNA-seq) is to perform variant calling from individual samples, with the drawback that only variable posit...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6587293/ https://www.ncbi.nlm.nih.gov/pubmed/31249686 http://dx.doi.org/10.1186/s40104-019-0359-0 |
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author | Brouard, Jean-Simon Schenkel, Flavio Marete, Andrew Bissonnette, Nathalie |
author_facet | Brouard, Jean-Simon Schenkel, Flavio Marete, Andrew Bissonnette, Nathalie |
author_sort | Brouard, Jean-Simon |
collection | PubMed |
description | The Genome Analysis Toolkit (GATK) is a popular set of programs for discovering and genotyping variants from next-generation sequencing data. The current GATK recommendation for RNA sequencing (RNA-seq) is to perform variant calling from individual samples, with the drawback that only variable positions are reported. Versions 3.0 and above of GATK offer the possibility of calling DNA variants on cohorts of samples using the HaplotypeCaller algorithm in Genomic Variant Call Format (GVCF) mode. Using this approach, variants are called individually on each sample, generating one GVCF file per sample that lists genotype likelihoods and their genome annotations. In a second step, variants are called from the GVCF files through a joint genotyping analysis. This strategy is more flexible and reduces computational challenges in comparison to the traditional joint discovery workflow. Using a GVCF workflow for mining SNP in RNA-seq data provides substantial advantages, including reporting homozygous genotypes for the reference allele as well as missing data. Taking advantage of RNA-seq data derived from primary macrophages isolated from 50 cows, the GATK joint genotyping method for calling variants on RNA-seq data was validated by comparing this approach to a so-called “per-sample” method. In addition, pair-wise comparisons of the two methods were performed to evaluate their respective sensitivity, precision and accuracy using DNA genotypes from a companion study including the same 50 cows genotyped using either genotyping-by-sequencing or with the Bovine SNP50 Beadchip (imputed to the Bovine high density). Results indicate that both approaches are very close in their capacity of detecting reference variants and that the joint genotyping method is more sensitive than the per-sample method. Given that the joint genotyping method is more flexible and technically easier, we recommend this approach for variant calling in RNA-seq experiments. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40104-019-0359-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6587293 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65872932019-06-27 The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments Brouard, Jean-Simon Schenkel, Flavio Marete, Andrew Bissonnette, Nathalie J Anim Sci Biotechnol Short Report The Genome Analysis Toolkit (GATK) is a popular set of programs for discovering and genotyping variants from next-generation sequencing data. The current GATK recommendation for RNA sequencing (RNA-seq) is to perform variant calling from individual samples, with the drawback that only variable positions are reported. Versions 3.0 and above of GATK offer the possibility of calling DNA variants on cohorts of samples using the HaplotypeCaller algorithm in Genomic Variant Call Format (GVCF) mode. Using this approach, variants are called individually on each sample, generating one GVCF file per sample that lists genotype likelihoods and their genome annotations. In a second step, variants are called from the GVCF files through a joint genotyping analysis. This strategy is more flexible and reduces computational challenges in comparison to the traditional joint discovery workflow. Using a GVCF workflow for mining SNP in RNA-seq data provides substantial advantages, including reporting homozygous genotypes for the reference allele as well as missing data. Taking advantage of RNA-seq data derived from primary macrophages isolated from 50 cows, the GATK joint genotyping method for calling variants on RNA-seq data was validated by comparing this approach to a so-called “per-sample” method. In addition, pair-wise comparisons of the two methods were performed to evaluate their respective sensitivity, precision and accuracy using DNA genotypes from a companion study including the same 50 cows genotyped using either genotyping-by-sequencing or with the Bovine SNP50 Beadchip (imputed to the Bovine high density). Results indicate that both approaches are very close in their capacity of detecting reference variants and that the joint genotyping method is more sensitive than the per-sample method. Given that the joint genotyping method is more flexible and technically easier, we recommend this approach for variant calling in RNA-seq experiments. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40104-019-0359-0) contains supplementary material, which is available to authorized users. BioMed Central 2019-06-21 /pmc/articles/PMC6587293/ /pubmed/31249686 http://dx.doi.org/10.1186/s40104-019-0359-0 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Short Report Brouard, Jean-Simon Schenkel, Flavio Marete, Andrew Bissonnette, Nathalie The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments |
title | The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments |
title_full | The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments |
title_fullStr | The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments |
title_full_unstemmed | The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments |
title_short | The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments |
title_sort | gatk joint genotyping workflow is appropriate for calling variants in rna-seq experiments |
topic | Short Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6587293/ https://www.ncbi.nlm.nih.gov/pubmed/31249686 http://dx.doi.org/10.1186/s40104-019-0359-0 |
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