Reporter assay using the beta-galactosidase (lacZ) gene. The lacZ gene is typically fused to the promoter of interest. Differential regulation of the promoter mediated by the TF is assessed by induction of the system and evaluation of lacZ expression. Bacteria expressing lacZ appear blue when grown on a X-gal medium. The assay is often performed using a plasmid borne construction on a lacZ(def) strain.

DNA-arrays (or DNA-chips or microarrays) are flat slabs of glass, silicon or plastic onto which thousands of multiple short single-stranded (ss) DNA sequences (corresponding to small regions of a genome) have been attached. After performing a mRNA extraction in induced and non-induced cells, the mRNA is again reverse transcribed, but here the reaction is tweaked, so that the emerging cDNA contains nucleotides marked with different fluorophores for controls and experiment. Targets will hybridize by base-pairing with those probes that resemble them the most. The array can then be stimulated by a laser and scanned for fluorescence at two different wavelengths (control and induced). The ratio or log-ratio between the two fluorescence intensities corresponds to the induction level.

The DNAse foot-printing method starts by focusing on a given region of interest (e.g. a promoter region) and amplifying it by PCR to obtain lots of sample. It then throws in the TF and then the DNAse. The mix is left to stir for a short time and then gel electrophoresis is run to compare the pattern of fragments in a control (no TF) and in the sample. If the TF has bound the sample, it will have protected a stretch of DNA (encompassing some fragments of the control) and thus those fragments will not appear in the sample gel. The fragments can then be cut-out from the gel, purified and sequenced to obtain the sequence of the protected region. This is often used to identify the binding motif of a TF for the first time. The foot-printing will typically resolve the protected region down to 50-100 bp, and the sequence can be then examined for possible TF-binding sites either by eye of using a computer search.

Machine learning methods can be used to predict TF-binding sites, often using additional sequence-derived information (e.g. predicted DNA curvature), partitioning classical PSSMs into submotifs or computing correlations among site positions. These methods can yield improved predictions, but their efficiency must be properly assessed.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.