Hache Hendrik, Wierling Christoph, Lehrach Hans, Herwig Ralf
Vertebrate Genomics-Bioinformatics Group, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany.
Bioinformatics. 2009 May 1;25(9):1205-7. doi: 10.1093/bioinformatics/btp115. Epub 2009 Feb 27.
The analysis of gene regulatory networks (GRNs) is a central goal of bioinformatics highly accelerated by the advent of new experimental techniques, such as RNA interference. A battery of reverse engineering methods has been developed in recent years to reconstruct the underlying GRNs from these and other experimental data. However, the performance of the individual methods is poorly understood and validation of algorithmic performances is still missing to a large extent. To enable such systematic validation, we have developed the web application GeNGe (GEne Network GEnerator), a controlled framework for the automatic generation of GRNs. The theoretical model for a GRN is a non-linear differential equation system. Networks can be user-defined or constructed in a modular way with the option to introduce global and local network perturbations. Resulting data can be used, e.g. as benchmark data for evaluating GRN reconstruction methods or for predicting effects of perturbations as theoretical counterparts of biological experiments.
Available online at http://genge.molgen.mpg.de.
Supplementary data are available at Bioinformatics online.
基因调控网络(GRN)分析是生物信息学的核心目标,随着RNA干扰等新实验技术的出现,这一目标的实现速度大幅加快。近年来,已经开发了一系列逆向工程方法,用于从这些及其他实验数据中重建潜在的基因调控网络。然而,人们对各个方法的性能了解甚少,算法性能的验证在很大程度上仍然缺失。为了实现这种系统验证,我们开发了网络应用程序GeNGe(基因网络生成器),这是一个用于自动生成基因调控网络的可控框架。基因调控网络的理论模型是一个非线性微分方程系统。网络可以由用户定义或以模块化方式构建,并可选择引入全局和局部网络扰动。所得数据可用于,例如作为评估基因调控网络重建方法的基准数据,或作为生物学实验的理论对应物来预测扰动的影响。
可在http://genge.molgen.mpg.de在线获取。
补充数据可在《生物信息学》在线获取。