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The above probabilistic modeling framework that incorporates multiple motif models can be viewed as an extension of a framework proposed in [42].
We use the same approach to adopt traditional promoter scanning to handle multiple motif models and report the smallest p-value over the multiple motif models.
The same approach to handle multiple motif models is used with the method from [63].
Most importantly, the proposed methods can make principled inference from multiple data sources that can include, among others, multiple motif models, evolutionary conservation, regulatory potential, CpG islands, nucleosome positioning, DNase hypersensitive sites and ChIP-chip.
A preliminary version of the computational methods presented in Sections 'Modeling framework', 'Likelihood approach: one motif model θ', and 'Likelihood approach: multiple motif models Θ' have been reported in our previous conference article [88].
Each motif model was linked to a transcription factor gene, allowing for redundancies in the motif databases; multiple TFs were allowed to be paired with the same motif, and many TFs were represented by multiple motif models.
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Given that our proposed method is based on a multiple-motif model, we believe it can be applied to other TFs, with some modifications, and may serve as a basic tool to discover important cis-regulatory features.
As noted in Section 3.3, the cut heuristic in combination with the ZOOPS model allows discovery of multiple motif occurrences within a single input sequence.
Define as the configuration of motif models from in ; that is, specifies the motif model, which begins from location and has a length.
Motif models were compared with MotifComparison [26].
The main difference between a single TF and multiple TFs regulating a gene is that combinatorial regulation requires all TFs to have at least one binding site for (at least) one of their motif models.
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