Accurate calling of SNPs and genotypes from next-generation sequencing data is an essential prerequisite for most human genetics studies. A number of computational steps are required or recommended when translating the raw sequencing data into the final calls. However, whether each step does contribute to the performance of variant calling and how it affects the accuracy still remain unclear, making it difficult to select and arrange appropriate steps to derive high quality variants from different sequencing data. In this study, we made a systematic assessment of the relative contribution of each step to the accuracy of variant calling from Illumina DNA sequencing data. We found that the read preprocessing step did not improve the accuracy of variant calling, contrary to the general expectation. Although trimming off low-quality tails helped align more reads, it introduced lots of false positives. The ability of markup duplication, local realignment and recalibration, to help eliminate false positive variants depended on the sequencing depth. Rearranging these steps did not affect the results. The relative performance of three popular multi-sample SNP callers, SAMtools, GATK, and GlfMultiples, also varied with the sequencing depth. Our findings clarify the necessity and effectiveness of computational steps for improving the accuracy of SNP and genotype calls from Illumina sequencing data and can serve as a general guideline for choosing SNP calling strategies for data with different coverage.
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