The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments
The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments
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DOI：
10.1186/s40104-019-0359-0
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发表时间：
2019-06-21
影响因子：
7
通讯作者：
Bissonnette, Nathalie
中科院分区：
文献类型：
--
作者：
Brouard, Jean-Simon;Schenkel, Flavio;Bissonnette, Nathalie
基因组分析工具包（GATK）是一套流行的程序，用于从下一代测序数据中发现和基因分型变体。目前GATK对RNA测序（RNA-seq）的建议是从单个样品中进行变异识别，缺点是只报告可变位置。GATK的3.0及以上版本提供了在基因组变异调用格式（GVCF）模式下使用HaplotypeCaller算法调用样本队列上的DNA变异的可能性。使用这种方法，每个样本上的变异被单独调用，每个样本生成一个GVCF文件，列出基因型可能性及其基因组注释。在第二步中，通过联合基因分型分析从GVCF文件中调用变体。与传统的联合发现工作流程相比，该策略更灵活，并且减少了计算挑战。使用GVCF工作流程来挖掘RNA-seq数据中的SNP提供了实质性的优势，包括报告参考等位基因的纯合基因型以及缺失数据。利用从50头奶牛中分离的原代巨噬细胞获得的RNA-seq数据，通过将该方法与所谓的每样本方法进行比较，验证了用于在RNA-seq数据上识别变体的GATK联合基因分型方法。此外，使用来自一项伴随研究的DNA基因型对两种方法进行了成对比较，以评价其各自的灵敏度、精密度和准确度，该研究包括使用测序基因分型或牛SNP 50微珠芯片（插补至牛高密度）对相同的50头奶牛进行基因分型。结果表明，这两种方法在检测参考变体的能力上非常接近，并且联合基因分型方法比每个样本方法更灵敏。鉴于联合基因分型方法更灵活，技术上更容易，我们推荐这种方法用于RNA-seq实验中的变体调用。
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.