Author ORCID Identifier
https://orcid.org/0000-0001-9419-4384
https://orcid.org/0000-0003-3039-2159
https://orcid.org/0000-0002-2131-7651
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N/A
N/A
Document Type
Article
Publication Date
2013
College/Unit
Davis College of Agriculture, Natural Resources and Design
Department/Program/Center
Division of Plant and Soil Sciences
Abstract
Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i.e., Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. To achieve this flexibility and improve prediction performance, we have implemented multiple mainstream GRN prediction algorithms including co-expression, Graphical Gaussian Models (GGMs), Context Likelihood of Relatedness (CLR), and parallelized versions of TIGRESS and GENIE3. Besides these existing algorithms, we also proposed a parallel Bayesian network learning algorithm, which can infer causal relationships (i.e., directionality of interaction) and scale up to several thousands of genes. Moreover, this web server also provides tools to allow integrative and comparative analysis between predicted GRNs obtained from different algorithms or experiments, as well as comparisons between legume species. The web site is available at http://legumegrn.noble.org.
Digital Commons Citation
Wang, Mingyi; Verdier, Jerome; Benedito, Vagner A.; Tang, Yuhong; Murray, Jeremy D.; Ge, Yinbing; and Becker, Jorg D., "LegumeGRN: A Gene Regulatory Network Prediction Server for Functional and Comparative Studies" (2013). Faculty & Staff Scholarship. 2626.
https://researchrepository.wvu.edu/faculty_publications/2626
Source Citation
Wang M, Verdier J, Benedito VA, Tang Y, Murray JD, Ge Y, et al. (2013) LegumeGRN: A Gene Regulatory Network Prediction Server for Functional and Comparative Studies. PLoS ONE 8(7): e67434. https://doi.org/10.1371/journal.pone.0067434
Comments
© 2013 Wang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.