scholarly journals Reverse engineering and analysis of large genome-scale gene networks

2012 ◽  
Vol 41 (1) ◽  
pp. e24-e24 ◽  
Author(s):  
Maneesha Aluru ◽  
Jaroslaw Zola ◽  
Dan Nettleton ◽  
Srinivas Aluru
2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Jun Li ◽  
Hairong Wei ◽  
Patrick Xuechun Zhao

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.


Genomics ◽  
2011 ◽  
Vol 97 (1) ◽  
pp. 7-18 ◽  
Author(s):  
Varun Narendra ◽  
Nikita I. Lytkin ◽  
Constantin F. Aliferis ◽  
Alexander Statnikov

2017 ◽  
Author(s):  
Tak Lee ◽  
Sohyun Hwang ◽  
Chan Yeong Kim ◽  
Hongseok Shim ◽  
Hyojin Kim ◽  
...  

Gene networks provide a system-level overview of genetic organizations and enable the dissection of functional modules underlying complex traits. Here we report the generation of WheatNet, the first genome-scale functional network for T. aestivum and a companion web server (www.inetbio.org/wheatnet). WheatNet was constructed by integrating 20 distinct genomics datasets, including 156,000 wheat-specific co-expression links mined from 1,929 microarray data. A unique feature of WheatNet is that each network node represents either a single gene or a group of genes. We computationally partitioned gene groups mimicking homeologous genes by clustering 99,386 wheat genes, resulting in 20,248 gene groups comprising 63,401 genes and 35,985 individual genes. Thus, WheatNet was constructed using 56,233 nodes, and the final integrated network has 20,230 nodes and 567,000 edges. The edge information of the integrated WheatNet and all 20 component networks are available for download.


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