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It should be possible to combine these sorts of modeling approaches with surveillance data to gauge the likelihood that a sampled variant will spread through the population.
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It then generates a diploid genome containing the sampled variants with an enhanced version of vcf2diploid (Rozowsky et al., 2011) (see Supplementary Material).
In the beginning of a simulation we generate five random repertoires by sampling variants from this pool, and we infect 10 random hosts with each of these repertoires.
A sample variant input file for ClinLabGeneticist.
Multi-sample variant calling was performed as described in Methods.
The use of multi sample variant detection to identify SNVs improved NRS but worsened NRD compared to single sample variant detection.
Multi-sample variant calling facilitated the detection of 12.1 million variants.
Subsequent multi-sample variant calling with mpileup [ 48] yielded genotypes at 17.17 million sites.
The results of single sample variant detection are in Figure 1.
Second, multi-sample variant calling across all samples, using Unified Genotyper with the same parameters as the single-sample call.
In this study, 16.4 million SNV positions (unfiltered) were found by all four multi sample variant detection methods, and 1.7 million InDels were found by all four multi sample variant detection methods.
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