## 2 Binding site representation
Top-left is the collection of binding sites, each of which is called an oligo or conserved sequence; oligos can be aligned with gaps to maximize the motif content but in this case, it is a gap-free alignment. Several models have been displayed and lastly an advance model of PWM is presented [47]; the normalized formula is inferred from the original equation to ensure the rule that the fitness score of a matched oligo can be estimated by taking the sum of the fitness at each position. The 'bold' part is the core region of the binding sites i.e. the most conserved region in the binding motif model. Bottom-right is the sequence logo that can quickly visualize the specificity of the conserved information in each column. **(b)** A brief look on the history of binding motif models. Starting from the first simple representation, consensus sequences, one has developed more advance models to characterize the binding motifs of TFs. However, due to the nature of the binding sites e.g. short, degenerate, etc., the problem has become a challenge and the proposed strategies have been modified when applied to higher eukaryotes e.g. search for composite motifs (a set of TFBSs) instead of single motifs, combining additional lines of biological evidence in detecting TFBSs (phylogeny, co-expression, and/or co-function). ETS1 binding sites  
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training data [46]. Therefore, emphasis has been given to strategies that optimize the PWM instead of building more complicated models. For example, the scores in the cells of the matrix can be transformed to improve the specificity of the binding motif model (e.g. convert frequencies to probabilities, adding pseudo-count, taking logarithms, etc. [30, 37]) and the binding sites can be aligned before creating the PWM [35]. In some cases, the information content (IC) of the PWM, or some similar form, is made use to select a suitable number of binding sites for creating the binding motif model [30, 47, 48]; \(IC=\sum\limits_{i}\sum\limits_{b\in\{A,C,G,T\}}f_{b,j}\log_{2}\frac{f_{b,j}}{P _{b}}\) where \(f_{b,j}\) is the observed frequency of base \(b\) at position \(i\) and \(p_{b}\) is the background frequency of base \(b\) (usually 25% as neutral distribution across the genome is assumed). Additionally, other significant efforts have been devoted towards enhancing the power of the PWM in order to better discriminate between real binding sites and the background e.g. random data or non-regulatory regions (**Figure 1b**). In this direction, [PERSON] et al. [49] proposed 16-row matrices to replace the 4-row PWMs; [PERSON] et al. [50] tried to classify TFBSs into TF families based on the constrained binding sequence diversity for groups of structurally related TFs to create familial binding profiles; [PERSON] et al. [51] computationally divided the binding site collection of a TF into two subsets corresponding to two-child PWMs to increase the binding specificity of TF profiles. As earlier noted, however, the short length of the binding sites makes them appear fairly redundant and predictive methods are often replete with false positives. Therefore, given that the main question concerns the actual identification of TFBSs and effective the location of the promoter, searching becomes a more critical issue than simply optimizing the representation.