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Computational predictions rely on sequence specificities that are typically taken from a database [4] or obtained as an output from a motif discovery method [6].
WaRSwap is a randomization algorithm that for the first time provides a practical network motif discovery method for large multi-layer networks, for example those that include transcription factors, microRNAs, and non-regulatory protein coding genes.
We therefore reasoned that a multiple species motif discovery method might increase performance.
By gradually forcing all pseudo counts to be equal (unity), i.e., increasing the uncertainty, the Bayesian binding prediction method indeed turns into a pure motif discovery method.
Using MEME and COVE, we explain our next method, CMfinder, a state of the art motif discovery method.
Here, we report a new motif discovery method that can analyze tens of thousands of DNA sequences each ~1 kbp long.
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FoldMiner is unique among such algorithms in that it improves both the motif definition and the sensitivity of a structural similarity search by combining the search and motif discovery methods and using information from each process to enhance the other.
We clustered test sets and motif discovery methods according to motif discovery success; see Figure 3.
Expression, binding, and cross-species conservation data have all been used to guide motif discovery methods.
A good overview of motif discovery methods and applications is provided in [7].
This platform allowed us to estimate the accuracy of our motif discovery methods.
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