Pham Tho Hoan, Clemente José Carlos, Satou Kenji, Ho Tu Bao
Japan Advanced Institute of Science and Technology, Asahidai, Nomi, Ishikawa, Japan.
Bioinformatics. 2005 Sep 1;21 Suppl 2:ii101-7. doi: 10.1093/bioinformatics/bti1117.
Even in a simple organism like yeast Saccharomyces cerevisiae, transcription is an extremely complex process. The expression of sets of genes can be turned on or off by the binding of specific transcription factors to the promoter regions of genes. Experimental and computational approaches have been proposed to establish mappings of DNA-binding locations of transcription factors. However, although location data obtained from experimental methods are noisy owing to imperfections in the measuring methods, computational approaches suffer from over-prediction problems owing to the short length of the sequence motifs bound by the transcription factors. Also, these interactions are usually environment-dependent: many regulators only bind to the promoter region of genes under specific environmental conditions. Even more, the presence of regulators at a promoter region indicates binding but not necessarily function: the regulator may act positively, negatively or not act at all. Therefore, identifying true and functional interactions between transcription factors and genes in specific environment conditions and describing the relationship between them are still open problems.
We developed a method that combines expression data with genomic location information to discover (1) relevant transcription factors from the set of potential transcription factors of a target gene; and (2) the relationship between the expression behavior of a target gene and that of its relevant transcription factors. Our method is based on rule induction, a machine learning technique that can efficiently deal with noisy domains. When applied to genomic location data with a confidence criterion relaxed to P-value = 0.005, and three different expression datasets of yeast S.cerevisiae, we obtained a set of regulatory rules describing the relationship between the expression behavior of a specific target gene and that of its relevant transcription factors. The resulting rules provide strong evidence of true positive gene-regulator interactions, as well as of protein-protein interactions that could serve to identify transcription complexes.
Supplementary files are available from http://www.jaist.ac.jp/~h-pham/regulatory-rules
即使在像酿酒酵母这样的简单生物体中，转录也是一个极其复杂的过程。基因集的表达可以通过特定转录因子与基因启动子区域的结合来开启或关闭。已经提出了实验和计算方法来建立转录因子DNA结合位置的映射。然而，尽管从实验方法获得的位置数据由于测量方法的不完善而存在噪声，但计算方法由于转录因子结合的序列基序长度较短而存在过度预测问题。此外，这些相互作用通常依赖于环境：许多调节因子仅在特定环境条件下与基因的启动子区域结合。更重要的是，调节因子在启动子区域的存在表明了结合，但不一定意味着功能：调节因子可能起正向作用、负向作用或根本不起作用。因此，在特定环境条件下识别转录因子与基因之间真正的功能相互作用并描述它们之间的关系仍然是未解决的问题。
我们开发了一种方法，该方法将表达数据与基因组位置信息相结合，以发现（1）目标基因潜在转录因子集中的相关转录因子；以及（2）目标基因的表达行为与其相关转录因子的表达行为之间的关系。我们的方法基于规则归纳，这是一种能够有效处理噪声领域的机器学习技术。当应用于置信度标准放宽到P值 = 0.005的基因组位置数据以及酿酒酵母的三个不同表达数据集时，我们获得了一组描述特定目标基因的表达行为与其相关转录因子之间关系的调控规则。所得规则为真正的基因 - 调节因子相互作用以及可用于识别转录复合物的蛋白质 - 蛋白质相互作用提供了有力证据。
