Bioinformatics
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Comparative Study
Rank order metrics for quantifying the association of sequence features with gene regulation.
Genome sequences and transcriptome analyses allow the correlation between gene regulation and DNA sequence features to be studied at the whole-genome level. To quantify these correlations, metrics are needed that can be applied to any sequence feature, regardless of its statistical distribution. It is also desirable for the metric values to be determined objectively, that is, without the use of subjective threshold values. ⋯ A Python program for calculating the ROC AUC and MNCP metric values given input rank orders is available from ftp://ftp.bs.jhmi.edu/users/nclarke/MNCP/