背景:测序基因分型(GBS)提供了使用数百万个标记对数百个个体进行基因分型的负担得起的方法。然而,这对生物信息学程序提出了挑战,这些程序必须克服可能的伪影,例如聚合酶链反应重复产生的偏倚和测序错误。基因分型错误导致数据偏离常规减数分裂的预期。这个,反过来,导致标记的分组和排序困难,导致膨胀和不正确的链接图。因此,基因分型错误可以很容易地通过连锁图质量评估来检测。
结果:我们开发并使用Reads2Map工作流程,利用二倍体异交种群的模拟和经验GBS数据构建连锁图。工作流运行GATK,Stacks,TASSEL,和Freebayes的单核苷酸多态性呼唤和向上发展,polyRAD,和SuperMASSA用于基因型调用,以及OneMap和GUSMap来构建链接图。使用模拟数据,我们观察到哪种基因型调用软件无法识别GBS测序数据中的常见错误,并提出了特定的过滤器来更好地处理这些错误.我们测试了是否可以使用每个软件的基因型概率或全局错误率来克服连锁图中的错误,以使用更新版本的OneMap来估计遗传距离。我们还评估了偏析变形的影响,污染物样本,和最终连锁图谱中基于单倍型的多等位基因标记。通过我们的评估,我们观察到,一些方法根据数据集(数据集相关)产生不同的结果,而另一些方法在它们之间产生一致的有利结果(数据集无关)。
结论:根据我们的结果,我们在Reads2Map工作流程中将显示对GBS数据集独立于数据集的方法设置为默认值。这减少了为其他经验数据集确定最佳管道和参数所需的测试数量。使用Reads2Map,用户可以选择最适合其数据上下文的管道和参数。Reads2MapApp闪亮的应用程序提供了结果的图形表示,以方便他们的解释。
Genotyping-by-sequencing (GBS) provides affordable methods for genotyping hundreds of individuals using millions of markers. However, this challenges bioinformatic procedures that must overcome possible artifacts such as the bias generated by polymerase chain reaction duplicates and sequencing errors. Genotyping errors lead to data that deviate from what is expected from regular meiosis. This, in turn, leads to difficulties in grouping and ordering markers, resulting in inflated and incorrect linkage maps. Therefore, genotyping errors can be easily detected by linkage map quality evaluations.
We developed and used the Reads2Map workflow to build linkage maps with simulated and empirical GBS data of diploid outcrossing populations. The workflows run GATK, Stacks, TASSEL, and Freebayes for single-nucleotide polymorphism calling and updog, polyRAD, and SuperMASSA for genotype calling, as well as OneMap and GUSMap to build linkage maps. Using simulated data, we observed which genotype call software fails in identifying common errors in GBS sequencing data and proposed specific filters to better handle them. We tested whether it is possible to overcome errors in a linkage map using genotype probabilities from each software or global error rates to estimate genetic distances with an updated version of OneMap. We also evaluated the impact of segregation distortion, contaminant samples, and haplotype-based multiallelic markers in the final linkage maps. Through our evaluations, we observed that some of the approaches produce different results depending on the dataset (dataset dependent) and others produce consistent advantageous results among them (dataset independent).
We set as default in the Reads2Map workflows the approaches that showed to be dataset independent for GBS datasets according to our results. This reduces the number of required tests to identify optimal pipelines and parameters for other empirical datasets. Using Reads2Map, users can select the pipeline and parameters that best fit their data context. The Reads2MapApp shiny app provides a graphical representation of the results to facilitate their interpretation.