Single Nucleotide Variant Calling in Polyploid Plant Genomes From Illumina Short Reads: A Practical Workflow Illustrated With Coffea arabica L

The detection of single nucleotide variants (SNVs) from DNA sequencing data is an essential approach for mutation discovery, enabling the identification of genetic variations that influence key traits, such as stress tolerance, productivity, and quality in crop species. A practical, step‐by‐step guide is presented for analyzing Illumina short reads for SNV calling in polyploid plant genomes using the command line in a Linux environment. As a worked example, the sequences used in this workflow were obtained from Coffea arabica L., a tetraploid species, whose leaves were subjected to chemical mutagenesis with ethyl methanesulfonate or sodium azide, inducing mutations that can be analyzed for genetic variation. The analysis begins with preprocessing raw sequencing data, including quality control and trimming, followed by read alignment to a reference genome. Key bioinformatics tools within the Linux environment are employed, such as FastQC for quality assessment, Trim Galore for optional adapter and low‐quality sequence removal, and BWA for sequence alignment. Variant calling is performed using the Genome Analysis Toolkit (GATK), with a focus on identifying SNVs resulting from mutagenic treatments. Further steps include filtering strategies to eliminate false positives and ensure high‐confidence variant calls, followed by functional annotation with SnpEff. Given the tetraploid nature of C. arabica , special considerations in SNV calling and data interpretation (i.e., ploidy setting, allele balance) are required to account for its complex genome structure. Common challenges encountered during the analysis are addressed, along with strategies for troubleshooting. By following this workflow, users gain foundational experience in bioinformatics, particularly in next‐generation sequencing data analysis, and the same framework can be transferred to other polyploid plant species. A workflow script with placeholders for all file names, a conda environment, and a container image are provided to automate the analysis. The workflow also contributes to research on genetic variation in C. arabica L.