Background: The rapid development of next-generation sequencing (NGS) technology has continuously been refreshing\nthe throughput of sequencing data. However, due to the lack of a smart tool that is both fast and accurate, the analysis\ntask for NGS data, especially those with low-coverage, remains challenging.\nResults: We proposed a decision-tree based variant calling algorithm. Experiments on a set of real data indicate that our\nalgorithm achieves high accuracy and sensitivity for SNVs and indels and shows good adaptability on low-coverage data. In\nparticular, our algorithm is obviously faster than 3 widely used tools in our experiments.\nConclusions: We implemented our algorithm in a software named Fuwa and applied it together with 4 well-known\nvariant callers, i.e., Platypus, GATK-UnifiedGenotyper, GATK-HaplotypeCaller and SAMtools, to three sequencing data\nsets of a well-studied sample NA12878, which were produced by whole-genome, whole-exome and low-coverage\nwhole-genome sequencing technology respectively. We also conducted additional experiments on the WGS data of 4\nnewly released samples that have not been used to populate dbSNP.
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