Tsai Huai-Kuang, Huang Grace Tzu-Wei, Chou Meng-Yuan, Lu Henry Horng-Shing, Li Wen-Hsiung
Genomics Research Center, Academia Sinica, Taipei, 115 Taiwan.
Bioinformatics. 2006 Jul 15;22(14):1675-81. doi: 10.1093/bioinformatics/btl160. Epub 2006 Apr 27.
Identifying transcription factor binding sites (TFBSs) is helpful for understanding the mechanism of transcriptional regulation. The abundance and the diversity of genomic data provide an excellent opportunity for identifying TFBSs. Developing methods to integrate various types of data has become a major trend in this pursuit.
We develop a TFBS identification method, TFBSfinder, which utilizes several data sources, including DNA sequences, phylogenetic information, microarray data and ChIP-chip data. For a TF, TFBSfinder rigorously selects a set of reliable target genes and a set of non-target genes (as a background set) to find overrepresented and conserved motifs in target genes. A new metric for measuring the degree of conservation at a binding site across species and methods for clustering motifs and for inferring position weight matrices are proposed. For synthetic data and yeast cell cycle TFs, TFBSfinder identifies motifs that are highly similar to known consensuses. Moreover, TFBSfinder outperforms well-known methods.
识别转录因子结合位点（TFBS）有助于理解转录调控机制。基因组数据的丰富性和多样性为识别TFBS提供了绝佳机会。开发整合各类数据的方法已成为这一研究领域的主要趋势。
我们开发了一种TFBS识别方法TFBSfinder，它利用了多种数据源，包括DNA序列、系统发育信息、微阵列数据和芯片杂交数据。对于一个转录因子，TFBSfinder会严格选择一组可靠的靶基因和一组非靶基因（作为背景集），以在靶基因中寻找过度富集和保守的基序。提出了一种衡量跨物种结合位点保守程度的新指标，以及用于基序聚类和推断位置权重矩阵的方法。对于合成数据和酵母细胞周期转录因子，TFBSfinder识别出的基序与已知的共有序列高度相似。此外，TFBSfinder的性能优于知名方法。
