## 1 Introduction
Traditionally, one has attempted to extract a set of promoters of a set of genes similar in function or expression pattern using a fixed-size length of the promoter sequence and then search for statistically overrepresented subsequences, also called motifs and considered as TFBSs if they match with some known TF profiles i.e. similar to known binding sites of a TF. A large number of tools have been developed using a variety of algorithmic approaches and underlying models. The relative advantages and disadvantages of each approach is making selection among them as well as developing novel tools become a non-trivial task. Consequently, one tried to build up testing datasets to measure their performance as well as identify the strengths and limitations [22-24]. Although there remain a number of difficulties in constructing the testing datasets and the accuracy measurements, a general view from these benchmarks is that _in silico_ predictions is still lack of corresponding to _in vivo_ experiments. The main reason is that binding motifs are short, degenerate and contain lack of information i.e. encoded by only four types of characters (A, T, C, and G), leading to the fact that most binding sites are found as random hits throughout the genomic DNA. Additionally, regulatory elements are not randomly distributed; they tend to form clusters with a particular structure, _cis_-regulatory modules [5, 25]. Therefore, recent studies have tried to combine with additional information such as gene expression, gene annotation, phylogenetic footprinting and/or search for composite motifs instead of single motifs to increase the sensitivity of the methods [25-28]. A number of excellent reviews have addressed a variety of critical issues. Typically, [PERSON] et al. [29] classified motif discovery methods following the motif model (deterministic or statistical, pattern driven or sequence driven), the scoring function, and the search strategy. [PERSON] et al. [30] provided a very comprehensive discussion about different algorithmic methods and approaches to the problem (similarly in [PERSON] [31] and [PERSON] [17]). And then since individual binding sites are lack of information for algorithmic methods and recent advances have moved to model the regulatory regions or combine with other biological lines of evidence, [PERSON] et al. [16] proposed an integrated framework to divide the trend following the description of motif discovering models such as single motifs, composite motifs (_cis_-regulatory modules), gene level - how several modules interact together to regulate a gene, and genome level - how several classes of modules interact together to regulate a set of genes. Alternatively, with the idea of exploring the interdependence between computational and experimental techniques in this aspect, [PERSON] et al. [32] made a summary on the synergism between _in silico_, _in vitro_ and _in vivo_ identification of transcription factor binding sites. And later, [PERSON] et al. [26] surveyed again different approaches combining with other biological evidence in motif discovery. Therefore, in this review we would like to concentrate on the developing strategies that detect physical TFBSs on the proximal promoters of target genes and some promising preliminary results on identifying functional-relevant binding sites. The remainder of this manuscript will discuss issues related to the representation of binding motifs, promoter identification and then review the basic approaches for the identification of TFBSs. Finally, we briefly explore the possibility to infer transcriptional regulatory networks under the aspect of promoter analysis.