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We could infer the activity of more transcription factors by using the motif based method with the accumulation of binding information in the transcription databases, such as the TRANSFAC [ 50].
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Both feature sets and their combinations yielded a very high accuracy, which is as good as the previously suggested sequence motif and k-mer based method.
Overall, the hybrid-property based method performs a little better than the motif-based method.
Höchsmann et al. [ 49] consider a local alignment based method to discover common local motifs from a pair of RNA sequences.
Here we report on a novel peptide library based method for HLA class II binding motif identification.
We proposed and developed an integrated approach to identify gene regulatory networks, which consists of a novel clustering method (namely motif-guided affinity propagation clustering (mAPC)) and a sampling based method (called Gibbs sampler based on outlier sum statistic (GibbsOS)).
Cluster-Buster, a Hidden Markov Model based method [ 93] was used to identify clusters of pre-specified motifs in a given gene cluster.
Genome wide searches of these motifs using information theory based methods identified thousands of possible SoxS binding sites.
This is because two of the stochastic learning based methods for RNA motif finding are extensions of MEME.
The stem based methods look at motifs as a certain (one of three) combination of stems with a purely secondary structure based focus.
Most of the alignment based methods we discussed discover motifs based on both sequence and structure similarity.
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