Localizing the binding sites of regulatory proteins is becoming increasingly feasible
and accurate. This is due to dramatic progress not only in chromatin immunoprecipitation
combined by next-generation sequencing (ChIP-seq) but also in advanced statistical
analyses. A fundamental issue, however, is the alarming number of false positive predictions.
This problem can be remedied by improved peak calling methods of twin peaks, one at
each strand of the DNA, kernel density estimators, and false discovery rate estimations
based on control libraries. Predictions are filtered by de novo motif discovery in
the peak environments. These methods have been implemented in, among others, Valouev
et al.'s Quantitative Enrichment of Sequence Tags (QuEST) software tool. We demonstrate
the prediction of the human growth-associated binding protein (GABPalpha) based on
ChIP-seq observations.