Baur Brittany, Bozdag Serdar
Department of Math, Statistics and Computer Science, Marquette University , Milwaukee, Wisconsin.
J Comput Biol. 2015 Apr;22(4):289-99. doi: 10.1089/cmb.2014.0296.
One of the challenging and important computational problems in systems biology is to infer gene regulatory networks (GRNs) of biological systems. Several methods that exploit gene expression data have been developed to tackle this problem. In this study, we propose the use of copy number and DNA methylation data to infer GRNs. We developed an algorithm that scores regulatory interactions between genes based on canonical correlation analysis. In this algorithm, copy number or DNA methylation variables are treated as potential regulator variables, and expression variables are treated as potential target variables. We first validated that the canonical correlation analysis method is able to infer true interactions in high accuracy. We showed that the use of DNA methylation or copy number datasets leads to improved inference over steady-state expression. Our results also showed that epigenetic and structural information could be used to infer directionality of regulatory interactions. Additional improvements in GRN inference can be gleaned from incorporating the result in an informative prior in a dynamic Bayesian algorithm. This is the first study that incorporates copy number and DNA methylation into an informative prior in dynamic Bayesian framework. By closely examining top-scoring interactions with different sources of epigenetic or structural information, we also identified potential novel regulatory interactions.
系统生物学中具有挑战性且重要的计算问题之一是推断生物系统的基因调控网络（GRN）。已经开发了几种利用基因表达数据的方法来解决这个问题。在本研究中，我们提议使用拷贝数和DNA甲基化数据来推断基因调控网络。我们开发了一种基于典型相关分析对基因间调控相互作用进行评分的算法。在该算法中，拷贝数或DNA甲基化变量被视为潜在的调控变量，而表达变量被视为潜在的目标变量。我们首先验证了典型相关分析方法能够高精度地推断真实的相互作用。我们表明，使用DNA甲基化或拷贝数数据集相比稳态表达能改进推断效果。我们的结果还表明，表观遗传和结构信息可用于推断调控相互作用的方向性。通过将结果纳入动态贝叶斯算法的信息先验中，可以进一步改进基因调控网络推断。这是第一项将拷贝数和DNA甲基化纳入动态贝叶斯框架的信息先验中的研究。通过仔细研究具有不同表观遗传或结构信息来源的高分相互作用，我们还识别出了潜在的新型调控相互作用。
