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ARDEN
Specificity Control for Read Alignments Using an Artificial Reference
We introduce ARDEN (Artificial Reference Driven Estimation of false positives in NGS data), a novel benchmark that estimates error rates based on real experimental reads and an additionally generated artificial reference genome. It allows the computation of error rates specifically for a dataset and the construction of a ROC-curve. Thereby, it can be used to optimize parameters for read mappers, to select read mappers for a specific problem or also to filter alignments based on quality estimation.
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