ARDEN

Specificity Control for Read Alignments Using an Artificial Reference

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Description

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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Categories

Bio-Informatics

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Additional Project Details

User Interface

Command-line

Programming Language

Python

Registered

2013-03-04
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