DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models.
DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models.
复制标题
DOI：
10.1371/journal.pone.0013397
复制
发表时间：
2010-10-25
期刊：
影响因子：
3.7
通讯作者：
Bonneau R
中科院分区：
文献类型：
--
作者：
Greenfield A;Madar A;Ostrer H;Bonneau R
目前的技术已经导致了足够数量和质量的多种基因组数据类型的可用性，以作为自动全局网络推理的基础。因此，目前存在大量各种各样的网络推理方法，其学习调节网络到不同程度的细节。这些方法各有优缺点，因此可以相互补充。然而，以相辅相成的方式将不同的方法结合起来仍然是一项挑战。我们研究如何将三种可扩展的方法组合成一个有用的网络推理管道。第一种是一种新的基于t检验的方法，该方法依赖于一个全面的稳态敲除数据集来对监管相互作用进行排名。剩下的两个是以前公布的互信息和常微分方程为基础的方法（tlemetry和Inferelator 1.0，分别），使用时间序列和稳态数据来排名监管相互作用;后者具有额外的优势，也推断动态模型的基因调控，可用于预测系统的响应新的扰动。我们的基于t检验的方法在对调控相互作用进行排名方面被证明是强大的，在DREAM 4 100基因计算机网络推理挑战中率先采用了这些方法。我们证明了这种方法和两种方法之间的互补性，这两种方法利用时间序列数据相结合的三个成一个管道的能力，排名监管相互作用显着改善相比，单独的方法。此外，流水线能够准确地预测系统对新条件的响应（在这种情况下，新的双敲除遗传扰动）。我们对多种网络推理方法的性能进行了评估，为未来的方法开发提供了途径，并为基因组实验设计提供了简单的考虑。我们的代码可在http://err.bio.nyu.edu/inferelator/上公开获取。
Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge. We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test–based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively) that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations. Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations). Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/.
登录
查看更多内容
影响因子：
64.5
作者：
Bonneau, Richard;Facciotti, Marc T.;Baliga, Nitin S.
通讯作者：
Baliga, Nitin S.
影响因子：
30.8
作者：
Ihmels, J;Friedlander, G;Barkai, N
通讯作者：
Barkai, N
影响因子：
2.4
作者：
Bergmann, S;Ihmels, J;Barkai, N
通讯作者：
Barkai, N
影响因子：
46.9
作者：
di Bernardo, D;Thompson, MJ;Collins, JJ
通讯作者：
Collins, JJ
影响因子：
9.9
作者：
通讯作者：
--