Location: Cool and Cold Water Aquaculture ResearchTitle: Bottom-up GGM algorithm for constructing multiple layered hierarchical gene regulatory networks
|KUMARI, SAPNA - Michigan Technological University|
|DENG, WENPING - Michigan Technological University|
|GUNASEKARA, CHATHURA - Michigan Technological University|
|CHIANG, VINCENT - North Carolina State University|
|CHEN, HUANN-SHENG - National Institutes Of Health (NIH)|
|DAVIS, XIN - North Carolina State University|
|WEI, HAIRONG - Michigan State University|
Submitted to: BMC Bioinformatics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/9/2016
Publication Date: 3/18/2016
Publication URL: http://handle.nal.usda.gov/10113/63162
Citation: Kumari, S., Deng, W., Gunasekara, C., Chiang, V., Chen, H., Ma, H., Davis, X., Wei, H. 2016. Bottom-up GGM algorithm for constructing multiple layered hierarchical gene regulatory networks. BMC Bioinformatics. 17:132. doi.10.1186/s12859-016-0981-1.
Interpretive Summary: Many genes interacted with each other in a cell to produce a certain product. An algorithm called bottom-up graphic Gaussian model was developed in this paper to describe the relationship among the genes using small- to medium-sized microarray or RNA sequencing data sets. This algorithm was confirmed to be better than another widely used algorithm named ARACNE to build gene regulatory networks with both synthetic and real gene expression data sets. The algorithm can be used for research scientists to identify the regulated genes related to a given biological pathway of interest for experimental validation.
Technical Abstract: Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. A bottom-up graphic Gaussian model (GGM) algorithm was developed for constructing ML-hGRN operating above a biological pathway using small- to medium-sized microarray or RNA-seq data sets. The algorithm first placed genes of a pathway at the bottom layer and began to construct a ML-hGRN by evaluating all combined triple genes: two pathway genes and one regulatory gene. The algorithm retained all triple genes where a regulatory gene significantly interfered two paired pathway genes. The regulatory genes with highest interference frequency were kept as the second layer and the number kept is based on an optimization function. Thereafter, the algorithm was used recursively to build a ML-hGRN in layer-by-layer fashion until the defined number of layers was obtained or terminated automatically. We validated the algorithm and demonstrated its high efficiency in constructing ML-hGRNs governing biological pathways. The algorithm is instrumental for biologists to learn the hierarchical regulators associated with a given biological pathway from even small-sized microarray or RNA-seq data sets.