Abstract

Single nucleotide polymorphisms (SNPs), representing the most frequent form of genetic variation, serve as essential biomarkers for mapping complex traits, tracing evolutionary lineages, and identifying the genetic basis of disease susceptibility. Next-generation sequencing (NGS) enables large-scale SNP discovery across diverse organisms, yet accurate detection remains challenging due to sequencing errors, genome complexity, reference bias, and coverage depth. This narrative/comparative review synthesizes primary tool publications, benchmarking studies, and recent literature identified from PubMed, Google Scholar, Web of Science, and official software documentation. This review compares SNP detection programs such as GATK, BCFtools, FreeBayes, SAMtools, and DeepVariant and their algorithmic structures, namely pileup- based, haplotype-based, and machine-learning approaches. In general, the comparison suggests that no single tool is the best, as the performance of these tools largely depends on the organism type and genome complexity, sequencing platform, sequencing depth, type of variant, and computational resources. GATK is sensitive and precise among the detection tools reviewed, yet computationally intensive; BCFtools is fast and versatile for non-human datasets; FreeBayes is a high-precision tool for haplotype-based and multiallelic variants; SAMtools is a powerful and stable tool for low-coverage data; and DeepVariant achieves high precision through deep learning architectures but at a high computational cost. We address important hurdles in SNP detection, such as polyploidy, heterozygosity, and repetitive regions, alongside the emerging necessity of Pangenomic Equity, defined here as the use of diverse pangenome references to reduce ancestry-related reference bias in SNP discovery. Lastly, we discuss emerging developments, including artificial intelligence-based variant calling, transformer-based models, graph-based reference genomes, and pangenome-aware methods that help to overcome the limitations of linear genomic models. Therefore, this review summarizes the key considerations for tool selection and outlines future directions for robust and inclusive SNP discovery in genomic research.