A computational method of identifying false positives in the variant calling

We studied the strain-specific effect in genetic variant calling. To this end, we used two major strains of the rice genome, Indica and Japonica, and called the variant with models that are different in the composition of samples from the two strains. We found that the more the samples differed in their strains from the reference, the more variants were predicted. We used confusion matrices in machine learning methods to compare the performance of different variant calling models. We found that a significant portion of predicted variants are potential false positive variants. We then proposed a method to identify the false positives. The proposed method involves calling true variants from the purebred samples and the reference of the same strain. We demonstrated the validity of the proposed method on the different variant calling models.