Figure 3.

Results from example 1 – Single motif discovery benchmark. The figure shows the performance of MEME on the single motif discovery benchmark when guided respectively by a uniform positional priors track, a priors track based only on conservation, and a combined priors track made by automatically integrating information from several features with the use of a Priors Generator. The statistics were calculated by combining all sequences from the 22 datasets into one large dataset and measuring the overlap between the predicted binding sites and the target sites. The first eight statistics are nucleotide-level statistics whereas the last statistic is the site-level sensitivity (number of predicted sites overlapping with at least 25% of a target site). Due to the stochastic nature of the algorithm used to train the Priors Generator, the combined priors track could vary slightly depending on the training. We therefore trained 20 different Priors Generators and ran MEME with priors tracks generated by each of them. The bars show the average scores with standard deviations over the 20 runs.

Klepper and Drabløs BMC Bioinformatics 2013 14:9   doi:10.1186/1471-2105-14-9
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