Accurately identifying DNA polymorphisms can bridge the gap between phenotypes and genotypes and is essential for molecular marker assisted genetic studies. Genome complexities, including large-scale structural variations, bring great challenges to bioinformatic analysis for obtaining high-confidence genomic variants, as sequence differences between non-allelic loci of two or more genomes can be misinterpreted as polymorphisms. It is important to correctly filter out artificial variants to avoid false genotyping or estimation of allele frequencies. Here, we present an efficient and effective framework, inGAP-family, to discover, filter, and visualize DNA polymorphisms and structural variants (SVs) from alignment of short reads. Applying this method to polymorphism detection on real datasets shows that elimination of artificial variants greatly facilitates the precise identification of meiotic recombination points as well as causal mutations in mutant genomes or quantitative trait loci. In addition, inGAP-family provides a user-friendly graphical interface for detecting polymorphisms and SVs, further evaluating predicted variants and identifying mutations related to genotypes. It is accessible at https://sourceforge.net/projects/ingap-family/.

准确识别 DNA 多态性可以弥合表型和基因型之间的差距，对于分子标记辅助遗传研究至关重要。基因组的复杂性，包括大规模的结构变异，为获得高可信度基因组变异的生物信息学分析带来了巨大挑战，因为两个或多个基因组的非等位基因位点之间的序列差异可能被误解为多态性。正确过滤掉人工变异以避免错误的基因分型或等位基因频率估计非常重要。在这里，我们提出了一个高效且有效的框架，inGAP-family，从短读长的比对中发现、过滤和可视化 DNA 多态性和结构变异 (SV)。将该方法应用于真实数据集的多态性检测表明，消除人工变异极大地促进了减数分裂重组点的精确识别以及突变基因组或数量性状位点中的因果突变。此外，inGAP-family 提供了一个用户友好的图形界面，用于检测多态性和 SV，进一步评估预测的变异并识别与基因型相关的突变。可在 https://sourceforge.net/projects/ingap-family/ 访问。