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The McPromoter system output ranges from 0 to 1, and Table 5gives the results for a number of thresholds between 0.98 and 0.8; additional predictions below 0.7 are no longer distinguishable from randomly spaced predictions (see [ 34] for discussion).
By comparison with another predictor for Drosophila promoters, NNPP [ 11], the retrained McPromoter system has 10-15 times fewer false positives, but some of this improvement simply results from the larger training set and not from differences in the underlying algorithm.
We applied the first version of McPromoter trained on human data [ 37], which uses only sequence features and a simple Markov chain model for the whole promoter sequence.
We took advantage of our new large TSS set to retrain McPromoter (see Materials and methods).
It might also be useful to make training sets consisting of subsets of promoters that share combinations of motifs and use a collection of McPromoter variants each trained on a different class of core promoter.