SEME ( Sampling with Expectation maximization for Motif Elicitation) is a de novo motif discovery algorithm which uses pure probabilistic mixture model to model the motif’s binding features and uses expectation maximization (EM) algorithms to simultaneously learn the sequence motif, position, and sequence rank preferences without asking for any prior knowledge from the user.
SOMBRERO (The Self-Organizing Map for Biological Regulatory Element Recognition and Ordering) is a motif-finder that is based on the Self-Organizing Map neural network algorithm. In contrast to other probabilistic motif discovery tools, SOMBRERO poses motif-finding as a clustering problem. As such, SOMBRERO simultaneously estimates all motif signals in the input sequences (regulatory signals are separated from others during post-processing), as opposed to estimating each significant signal one-by-one
CMF (Contrast Motif Finder) Contrast motif finder that finds motifs with differential enrichment between two datasets.CMF aims to take advantage of multiple high quality binding datasets to identify subtle regulatory signals, such as context-dependent motifs, within bound sequences. It is specifically designed to discriminate between two sets of bound sequences and takes into account false positive sites when updating PWMs and other model parameters.