We have compared a recently developed module-based algorithm LeMoNe for reverse-engineering transcriptional regulatory networks to a mutual information based direct algorithm CLR, using benchmark expression data and databases of known transcriptional regulatory interactions for Escherichia coli and Saccharomyces cerevisiae. A global comparison using recall versus precision curves hides the topologically distinct nature of the inferred networks and is not informative about the specific subtasks for which each method is most suited. Analysis of the degree distributions and a regulator specific comparison show that CLR is 'regulator-centric', making true predictions for a higher number of regulators, while LeMoNe is 'target-centric', recovering a higher number of known targets for fewer regulators, with limited overlap in the predicted interactions between both methods. Detailed biological examples in E. coli and S. cerevisiae are used to illustrate these differences and to prove that each method is able to infer parts of the network where the other fails. Biological validation of the inferred networks cautions against over-interpreting recall and precision values computed using incomplete reference networks.
翻译:我们利用Escherichia coli和Sachharomyces cerevisiae的基准表达数据和已知转录管理互动数据库,将最近开发的用于逆向工程转录管理网络的模块算法LeMoNe与基于信息的相互信息直接算法CLR对照法进行了比较。使用回溯曲线和精确曲线进行的全球比较,隐藏了推断网络的地形特征与精确曲线的不同性质,对每种方法最适合的具体子任务没有提供信息。对度分布的分析以及监管者的具体比较表明,CLR是“Regulator-center ”,为更多监管者作出真实预测,而LeMoNe是“目标中心”,为较少监管者恢复了更多已知的目标,在两种方法的预测互动中都存在有限的重叠。 E.coli 和 S. Cerevisiae 的详细生物实例用来说明这些差异,并证明每种方法都能够推断出网络中其他失败的部分。对所推断的网络进行生物验证时告诫不要过度解释,使用不完整的参考网络计算准确值。