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.