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The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments.

ABSTRACT: 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.

SUBMITTER: Brouard JS

PROVIDER: S-EPMC6587293 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments.

Brouard Jean-Simon JS Schenkel Flavio F Marete Andrew A Bissonnette Nathalie N

Journal of animal science and biotechnology 20190621

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. Us ...[more]

PMID: 31249686

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Project description:BackgroundThe advent of next generation sequencing has opened new avenues for basic and applied research. One application is the discovery of sequence variants causative of a phenotypic trait or a disease pathology. The computational task of detecting and annotating sequence differences of a target dataset between a reference genome is known as "variant calling". Typically, this task is computationally involved, often combining a complex chain of linked software tools. A major player in this field is the Genome Analysis Toolkit (GATK). The "GATK Best Practices" is a commonly referred recipe for variant calling. However, current computational recommendations on variant calling predominantly focus on human sequencing data and ignore ever-changing demands of high-throughput sequencing developments. Furthermore, frequent updates to such recommendations are counterintuitive to the goal of offering a standard workflow and hamper reproducibility over time.ResultsA workflow for automated detection of single nucleotide polymorphisms and insertion-deletions offers a wide range of applications in sequence annotation of model and non-model organisms. The introduced workflow builds on the GATK Best Practices, while enabling reproducibility over time and offering an open, generalized computational architecture. The workflow achieves parallelized data evaluation and maximizes performance of individual computational tasks. Optimized Java garbage collection and heap size settings for the GATK applications SortSam, MarkDuplicates, HaplotypeCaller, and GatherVcfs effectively cut the overall analysis time in half.ConclusionsThe demand for variant calling, efficient computational processing, and standardized workflows is growing. The Open source Variant calling workFlow (OVarFlow) offers automation and reproducibility for a computationally optimized variant calling task. By reducing usage of computational resources, the workflow removes prior existing entry barriers to the variant calling field and enables standardized variant calling.

Dataset Information

The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments.

Publications

The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments.

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