fdrMotif is iterative and alternates between updating the position weight matrix (PWM) and significance testing. It starts with an initial PWM and a set of sequences (e.g., from ChIP experiments). It generates many sets of background (null) sequences under the input sequence probability model. At each model estimation step, fdrMotif determines the number of binding sites in each sequence by performing statistical tests. The FDR in the original dataset is controlled by monitoring the proportion of background subsequences that are declared as binding sites. The PWM is updated using an EM algorithm with two iterative steps (the E and M steps) until convergence. In the E-step, fdrMotif normalizes the sum of the probabilities over all position s in a sequence to the number of binding sites found in the sequence.
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Bioinformatics. 2008 Mar 1;24(5):629-36.
fdrMotif: identifying cis-elements by an EM algorithm coupled with false discovery rate control.
Li L, Bass RL, Liang Y.