Motifs - TAMO is developed around a unified motif representation of a position-specific scoring matrix (PSSM) (See MotifTools.py). Motif objects may be assembled from IUPAC-ambiguity codes, multiple sequence alignments, averages of other motifs, and matrices of frequencies or log-likelihood values. Motifs can printed, concatenated, indexed and sliced like text strings, or rendered as sequence logos. They can also be randomized, reverse-complemented, and recomputed using different assumptions about background base frequencies. Motifs can also store and report information about their origin, information content, and score. Finally, motifs can scan DNA sequences for instances of matching sites. We have included a command-line program Sitemap.py that uses the motif object to produce text-based maps of motif occurrences within a set of input sequences.
what a great timing on this one..I came to biostar for looking up motif discovery softwares..seems like a good place to start with my differential expressed genes from RNA-Seq data. I also have a question about how should I decide the clustering level to identity the set of coregulated genes..