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In the near future, a broad and systematic benchmarking study of multiple species phylogenetic footprinting procedures for motif enrichment and de novo motif discovery would advance the field.
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Our method improves the procedure for refining motif by understanding the details of the stability boundaries and by trying to escape out of the convergence region of the EM algorithm.
Upload of user-supplied area data is possible using the database procedure area__reload__proc or the webservice function calling this procedure, and similarly for motifs and data set attributes.
The whole procedure of motif search and candidate motif frequency matrix calculation is illustrated in Figure 3.
We extend RISOTTO, a combinatorial algorithm for motif discovery, by post-processing its output with a greedy procedure that uses prior information.
All procedures were implemented on a Linux workstation using a combination of Perl scripts interacting with the specified INCLUSIVE programs for motif analysis (MotifSampler, MotifSearch, MotifRanking) [ 17].
Multiple Em for Motif Elicitation.
Motif analysis was performed with the Multiple Em for Motif Elicitation (MEME) motif search tool.
Multiple expectation maximization for motif elicitation.
The MEME Suite (Multiple Expectation Maximization for Motif Elicitation) [50] was used for motif discovery in our previous study [31], and the discovered motifs were used.
The extrapolated rank estimation for motif extraction was 1,286.
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