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TFBS motif finding analysis supports our results.
Motif finding analysis applied to the identified c-Myc TFBSs supports our results and allowed us to predict many novel c-Myc target genes.
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Simonis et al. [ 23] combined a string-based motif finding method and linear discriminant analysis to identify motif combinations that can separate true regulons from false ones.
In [22], they find that the motif finding effort is greatly enhanced by referencing to nearby gene structure and identifying "peak regions" where motifs can be isolated.
In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes.
Indeed, the performance of motif finding algorithms improves significantly with respect to promoter analysis.
We observed that newer motif finding Web tools have the capability to find global over-represented motifs, local over-represented motifs, and alternative motifs.
iTriplet is a novel rule-based combinatorial or enumerative motif finding method that is able to process highly degenerate and long motifs that have resisted analysis by other methods.
Different motif finding Web tools implement different algorithms, which determine the results of the motif finding.
In addition, each motif finding Web tool has its own parameters set up for finding motifs.
However, it combines three existing motif finding tools for maximizing the chance obtaining true motifs.
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