DeGNServer: deciphering genome-scale gene networks through high performance reverse engineering analysis

Abstract

Analysis of genome-scale gene networks (GNs) using large-scale gene expression data provides unprecedented opportunities to uncover gene interactions and regulatory networks involved in various biological processes and developmental programs, leading to accelerated discovery of novel knowledge of various biological processes, pathways and systems. The widely used context likelihood of relatedness (CLR) method based on the mutual information (MI) for scoring the similarity of gene pairs is one of the accurate methods currently available for inferring GNs. However, the MI-based reverse engineering method can achieve satisfactory performance only when sample size exceeds one hundred. This in turn limits their applications for GN construction from expression data set with small sample size. We developed a high performance web server, DeGNServer, to reverse engineering and decipher genome-scale networks. It extended the CLR method by integration of different correlation methods that are suitable for analyzing data sets ranging from moderate to large scale such as expression profiles with tens to hundreds of microarray hybridizations, and implemented all analysis algorithms using parallel computing techniques to infer gene-gene association at extraordinary speed. In addition, we integrated the SNBuilder and GeNa algorithms for subnetwork extraction and functional module discovery. DeGNServer is publicly and freely available online.

Figure 1

The DeGNServer data analysis workflow.

Figure 2

Parallel implementation of the CLR method.

Figure 3

Average AUC scores from different association-based CLR methods for networks with larger and smaller numbers of expression profiles; Group A: networks constructed with smaller number of gene expression samples (30~90 samples), Group B: networks constructed with larger number of expression samples (100~1000). AUC scores were obtained through varying different threhold settings. A perfect model will have AUC score of 1, while random guessing will score an AUC around 0.5.

Figure 4

The identified subnetwork contains the essential transcription factors and other genes required for pluripotency maintenance. The twelve genes on the inner ring are transcription factors known to play essential or important role in pluripotency renewal of human embryonic stem cells. These include three master transcription factors, NANOG, POU5F1, and SOX2, which are absolutely required for pluripotency maintenance. The genes located on the outer ring were identified by DeGNServer for being closely coordinated with those transcription factors in the inner ring. The genes on outer ring, but highlighted in yellow, are those that are implicated by the existing literature to participate in the pluripotency renewal. This subnetwork was generated by using SNBuilder method [21] with NANOG, POU5F1, SOX2, and PHC1 as query seeds.

Figure 5

The subnetwork that is responsible for heart growth and development in mouse. The whole genome-scale network was constructed from 175 chips of GPL1261 platform using DeGNServer and then extracted using community-finding algorithm called GeNa [22] with Nkx2-5, Prox1, and Mef2c as query seeds. Genes highlighted in red are implicated by the existing literature to participate in heart growth and development.