Name
Abstract | ||
|---|---|---|
The discovery of gene regulatory elements requires the synergism between computational and experimental techniques in order to reveal the underlying regulatory mechanisms that drive gene expression in response to external cues and signals. Utilizing the large amount of high-throughput experimental data, constantly growing in recent years, researchers have attempted to decipher the patterns which are hidden in the genomic sequences. These patterns, called motifs, are potential binding sites to transcription factors which are hypothesized to be the main regulators of the transcription process. Consequently, precise detection of these elements is required and thus a large number of computational approaches have been developed to support the de novo identification of TFBSs. Even though novel approaches are continuously proposed and almost all have reported some success in yeast and other lower organisms, in higher organisms the problem still remains a challenge. In this paper, we therefore review the recent developments in computational methods for transcription factor binding site prediction. We start with a brief review of the basic approaches for binding site representation and promoter identification, then discuss the techniques to locate physical TFBSs, identify functional binding sites using orthologous information, and infer functional TFBSs within some context defined by additional prior knowledge. Finally, we briefly explore the opportunities for expanding these approaches towards the computational identification of transcriptional regulatory networks. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.3390/a2010582 | ALGORITHMS |
Keywords | Field | DocType |
transcription factor binding sites, binding site representation, promoter analysis, phylogenetic footprinting, context-specific, transcriptional regulatory networks | Binding site,Gene,Transcription (biology),DNA binding site,DECIPHER,Phylogenetic footprinting,Artificial intelligence,Bioinformatics,Computational biology,Mathematics,Machine learning,Transcription factor | Journal |
Volume | Issue | Citations |
2 | 1 | 5 |
PageRank | References | Authors |
0.59 | 47 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tung T Nguyen | 1 | 57 | 8.29 |
| Ioannis P. Androulakis | 2 | 87 | 9.42 |