## 4 Discovery of physical TFBSs
\begin{table}
\begin{tabular}{|l l|l l|} \hline \multicolumn{3}{|l|}{**Genome Browsers**} \\ UCSC & genome.ucsc.edu & VISTA & [[http://genome.lbl.gov/vista](http://genome.lbl.gov/vista)]([http://genome.lbl.gov/vista](http://genome.lbl.gov/vista)) \\ \hline \multicolumn{3}{|l|}{**Promoter resources**} \\ \hline _Databases_ & & \multicolumn{1}{l|}{_Prediction Tools_} \\ \hline Genomatix & genomatix.de/products/Gene2 Promoter & PromoterInspector & genomatix.de/promoterismpector.html \\ CSHL & rulai.cshl.edu/CSHLmpd2 & DragonGSF & research.i2r.a. \\ \cline{2-3} DBTSS & dbtss.hgc.jp & Eponine & www.sanger.ac.uk/Users/td2/enonine \\ EPD & www.epd.isb-sib.ch & FirstEF & rulai.cshl.org/tools/FirstEF \\ \hline \multicolumn{3}{|l|}{**Transcription factor resources**} \\ \hline _PWM databases_ & & \multicolumn{1}{l|}{_Phylogenetic footprinting tools_} \\ \hline Genomatix & genomatix.de/products/MatBase & FootPrinter & bio.cs.washington.edu/software.html/footprint \\ TRANSFAC & www.gene-regulation.com/publ/databases.html & PhyloME & bio.cs.washington.edu/software.html/phylyme \\ JASPAR & jaspar.cgb.ki.se & PhyloGibbs & www.phyloGibbs.unibas.ch/cgi-bin/phylosibbs.pl \\  & PhyloGibbs-MP & www.imsc.res.in/~rsidd/phylogibbs-mp \\  & MONKEY & rana.lbl.gov/monkey \\ \hline \multicolumn{3}{|l|}{_Single-motif discovery tools_} & \multicolumn{1}{l|}{_Cis-regulatory module discovery tools_} \\ \hline MatInspector & genomatix.de/products/MatInspector & FrameWorker & genomatix.de/framework.html \\ P-Match & www.gene-regulation.com/publ/irograms.html & CMA & www.gene-regulation.com/pub/programs.html \\ AlignACE & atlas med harvard.edu & CisModule & www.stat.ucla.edu/~zhou/CisModule \\ Consensus & bifrost.wustl.edu/consensus & CisPlusFinder & jakob.genetk uni- \\  & & kocla.debioinformatik/people/non/non.html \\ MEME & meme.sdsc.edu & DiRE & dire.decode.org \\ \hline \end{tabular}
\end{table} 
Table 1: Selected resources and relevant tools for _in silico_ TFBS identification. 30, 67]. Exhaustive search with pattern-based scoring (combinatorial category) is the starting point of discovering conserved motifs in a set of promoter sequences [67]. Due to magnitude of the search space, methods were further improved by exploring sequence-based exhaustive search [68] and also consensus search [69]. The probabilistic-based methods employ two main algorithms e.g. Gibbs sampling [65] and MEME [66] and have also been used extensively for motif discovery tools. The basic idea is to continuously reduce the search space and the false positive matches by more accurately representing the motif models. However, it is important to realize that although a large number of TFs has already been identified, and more are being identified, through numerous high-throughput activities emanating from the decoding of the human, _in silico_ analysis is further hindered by the fact that only a fraction of those can currently be mapped to known and well characterized profiles [53, 70, 71] (around 600 human TFs in www.genomatix.de vs. approximately 1,850 TFs found in human [72]). When conserved motifs are predicted computationally that are not present in available collections, these are then considered as novel binding sites and/or regulatory regions but they are set aside for further investigation. Therefore, besides such motif discovery methods, another approach to detect potential TFBSs is directly scanning known TF profiles and scoring to determine whether or not the matches are potential binding sites. Given that the scoring metric would assign relative importance to alternative binding sites in motif discovery methods [29, 73, 74], it is of equal importance to score directly the subsequences of interest in terms of their potential of being binding sites compared to known TF profiles. Despite the large number of alternative representation models and their associated scoring function, the most widely-used approach is still the one based on the PWM model and the sum fitness function, as discussed above.