## 3 Promoter identification  
The first step towards discovering TFBSs is identifying the set of promoters. In principle, they are defined as the upstream regions proximal to the transcription start sites (TSSs) of genes; however, their length is still not clearly defined among different studies although it is one of the most important factors affecting to the computational predictions. Numerous activities have been proposed such as the recent experiment known as genome-wide open chromatin map that integrates high-throughput sequencing and genome-wide titled array technologies has been performed to identify DNase I hypersensitive sites within human primary CD4\({}^{+}\) T cells [52]. Such activities aim at better defining proximal promoter lengths which are subsequently incorporated in commercial tools, such as [53].  
Besides experimentally identified promoters, a number of computational methods have been proposed to predict promoter regions. Available tools include PromoterInspector [54], DragonGSF [55], EnSemPro [56], and have all been thoroughly reviewed [57, 58]. Prediction tools can be classified into two main categories, signal-based approaches which rely on conserved signals relevant to promoters, e.g. TATA box, CAAT box, CpG islands, and content-based approaches that utilize conserved motifs to distinguish between promoters and non-promoter regions [59]. Several models have been shown to be promising but due to the complexities of the genome structure, large-scale predictions are still difficult [60].  
The structure of promoters, especially in mammals, is a complex which can be considered as a mini-structure of a gene where regulatory elements are interspersed within a large number of regionsnon-conserved and unknown function [60]. Traditionally, it has been assumed that the combinatorial interaction of multiple transcription factors with the gene promoter is sufficient to explain the process of transcription. However, recent studies provided results to show that a large proportion of mammalian genes possess multiple transcription start sites (TSSs) and thus multiple promoters driving gene expression in a context-specific manner [61-63]. Specifically, in a recent study [PERSON] at el. [15] developed and employed a custom microarray platform to show that nearly 35,000 alternative putative promoters are present on around 7,000 human genes. Furthermore, each set of unique combination of TFBSs in the promoter will determine its temporal and spatial expression in a specific context [60] (**Figure 2**). These observations significantly increase the complexity of understanding gene regulation and the transcription process in general, and create a huge challenge for both computational and experimental methodologies of TFBS identification.  
**Figure 2**. Data complexities in TFBS prediction. **(a)** Alternative promoters usually occur for genes in higher eukaryotes e.g. nearly 35,000 alternative putative promoters are present on around 7,000 human genes [15]. For a specific gene, different promoters are activated to drive the gene expression in different corresponding contexts. **(b)** Alternative sets of combinatorial TFs regulate the transcription process even though only one promoter is activated in these contexts. M\({}_{1}\), M\({}_{2}\), M\({}_{3}\) are three example transcriptional modules (a set of TFs or corresponding TFBSs) activated to regulate the transcription process; module M\({}_{1}\) is present on two cases whereas only a part of M\({}_{2}\) is functional in the other case e.g. human RANTES/CCL5 gene consists of different set of functional TFBSs in different cell types [60].