HIGHLIGHTS
SUMMARY
To solve this automatically, perSVade "optimize_parameters" generates simulated genomes (based on the reference genome and input dataset) with SVs and chooses the most accurate filters (with the highest harmonic mean between precision and recall (F-value)) for these simulations. Rationally design the parameters (based on parameters optimized for similar samples (see first point) and/or the benchmarking shown in this work) instead of inferring them with the "optimize_parameters" module for every new sample. Third, limit the simulations to a subset of chromosomes in the "optimize_parameters" module. To circumvent this, perSVade "optimize_parameters" can generate more . . .
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