Figure 3.

How PARma works. PARma is an iterative algorithm, which repeatedly executes three steps: based on a current model of the PAR-CLIP characteristics (left; see also Figure 6), scores are computed for each position in each cluster, which express the likelihood that the cluster is explained by the activity of the k-mer at this position (top right; see also Figure 7). These scores are fed into kmerExplain as prior probabilities, which then estimates k-mer activity probabilities using an EM algorithm (bottom). These k-mer activities in conjunction with data from the PAR-CLIP experiment (T to C conversions and RNase cleavage sites) are used to estimate the parameters of the PAR-CLIP model. We start this procedure by running kmerExplain on uniform scores and end it as soon as the model converges. EM, expectation maximization.

Erhard et al. Genome Biology 2013 14:R79   doi:10.1186/gb-2013-14-7-r79
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