![]() Received: ApAccepted: Published: July 3, 2013Ĭopyright: © 2013 Wang et al. ![]() PLoS ONE 8(7):Įditor: Enrique Hernandez-Lemus, National Institute of Genomic Medicine, Mexico (2013) LegumeGRN: A Gene Regulatory Network Prediction Server for Functional and Comparative Studies. Ĭitation: Wang M, Verdier J, Benedito VA, Tang Y, Murray JD, Ge Y, et al. 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. ![]() 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. 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. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. 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. 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. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. This state applies only to columns of the type Calculated.Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. ![]() Synta圎rror (9) The column is in an error state because of a syntax error in its expression. This state applies only to columns of the type Data. Incomplete (8) Some parts of the column have no data, and the column needs to be refreshed to bring the data in. This state applies only to columns of the type Calculated or CalculatedTableColumn. DependencyError (7) The column is in an error state because some of its calculation dependencies are in an error state. EvaluationError (6) The column is in an error state because of an error during expression evaluation. SemanticError (5) The column is in an error state because of an invalid expression. CalculationNeeded (4) The column is not queryable and needs to be refreshed (that is, recalculated) to become functional. This state is applicable only to columns of the type Data. NoData (3) The column is queryable but has no data. Ready (1) The column is queryable and has up-to-date data.
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