Workflow from Scientific Research

Open access visualization of Workflow, Flowchart, Illustration, Somatic SNVs, Sequencing Depth
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For the remaining loci, we identify putative somatic SNVs by focusing on ones if there is sufficient sequencing depth and alternative allele frequency (calibrated by a sequencing error model). The SVM module is designed to remove low-quality SNVs. The variant calling metrics including the QS for calling, VDB for filtering splice-site artifacts, MannWhitney U test of RPB, MannWhitney U test of BQB, MannWhitney U test of ratio of MQSB, SGB and BAF. The germline SNVs are considered as the positive training sets, while the continuous de novo SNV chunks (>2 SNVs) that do not include any germline SNV are set as the negative sets. The remaining de novo SNVs are considered as the test set.

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