Validation data for ChoCallate: A Computational Pipeline for Consensus Germline Variant Calling in Genomic Sequencing Data

Accurate germline variant calling in non-model and polyploid organisms, remains challenging due to sequencing errors, genomic complexity, and caller‑specific biases. Existing ensemble methods of variant calling are constrained by species‑specific training, limited portability, or an inability to process multiple samples concurrently. To address these limitations we developed ChoCallate, a Nextflow‑based pipeline for consensus germline variant calling. The pipeline integrates three species‑independent callers, bcftools, FreeBayes, and GATK HaplotypeCaller, within a flexible voting mechanism. A distinctive feature of ChoCallate is its incorporation of coverage‑aware consensus generation via BED file, enabling reliable variant identification even in reduced‑representation sequencing data such as RAD‑seq or RNA‑seq. Using a simulated Arabidopsis thaliana dataset containing 10,000 single‑nucleotide polymorphisms (SNPs) and 1,000 InDels across coverage depths ranging from $30 \times$ to $50 \times$, the two‑vote consensus strategy is demonstrated to optimize the precision‑recall trade‑off, particularly at low coverage ($3 \times$-$10 \times$), where precision for SNPs is enhanced without substantial loss of sensitivity. False‑negative variants were found to concentrate in centromeric and nucleolus organizer regions (NORs) across all strategies, indicating that repetitive genomic structure, rather than consensus logic, constitutes the primary limitation. In a case study of 200 bread wheat (Triticum aestivum) samples, ChoCallate identified 11,178 high‑confidence variants, and a genome‑wide association study (GWAS) for awnedness successfully recovered the known B1/ALI‑1 locus on chromosome 5A, replicating prior findings with reduced genomic inflation ($\lambda_{gc} = 1.06$). ChoCallate is portable, species‑agnostic, and freely available at https://github.com/alermol/ChoCallate.针对非模式生物与多倍体生物的生殖系变异精准检测（germline variant calling），始终因测序错误、基因组复杂性以及检测工具特异性偏倚而极具挑战性。现有变异检测集成方法受限于物种特异性训练、可移植性不足，或无法同时处理多个样本。为解决上述局限，我们开发了ChoCallate——一款基于Nextflow的生殖系变异共识检测流程。该流程整合了三种不依赖物种的变异检测工具：bcftools、FreeBayes与GATK HaplotypeCaller，并采用灵活的投票机制。ChoCallate的一项显著特性是通过BED文件实现覆盖度感知的共识生成，即便在简化代表性测序数据（如RAD-seq或RNA-seq）中，也可实现可靠的变异识别。本研究使用覆盖深度范围为30×至50×的模拟拟南芥（Arabidopsis thaliana）数据集，其中包含10000个单核苷酸多态性（single-nucleotide polymorphisms, SNPs）与1000个插入缺失（Insertions-deletions, InDels）。结果表明，双投票共识策略可优化精确率-召回率权衡，尤其在低覆盖度（3×至10×）条件下，此时单核苷酸多态性的精确率得到提升，且灵敏度未出现显著损失。所有检测策略下的假阴性变异均集中在着丝粒与核仁组织区（nucleolus organizer regions, NORs），这表明重复基因组结构而非共识逻辑是主要的局限性来源。在针对200份普通小麦（Triticum aestivum）样本的案例研究中，ChoCallate共鉴定出11178个高置信度变异；针对芒性的全基因组关联研究（genome-wide association study, GWAS）成功定位到了已知的5A染色体上的B1/ALI-1位点，并以更低的基因组膨胀系数（λ_gc = 1.06）重复了此前的研究结果。ChoCallate具备可移植性与物种无关性，可通过https://github.com/alermol/ChoCallate免费获取。