Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.7/487
Título: LegumeGRN: a gene regulatory network prediction server for functional and comparative studies
Autor: Wang, Mingyi
Verdier, Jerome
Benedito, Vagner A
Tang, Yuhong
Murray, Jeremy D
Ge, Yinbing
Becker, Jörg D
Carvalho, Helena
Rogers, Christian
Udvardi, Michael
He, Ji
Palavras-chave: Machine learning algorithms
Gene expression
Legumes
Gene regulatory networks
Medicago
Transcriptome analysis
Regulons
Gene regulation
Data: 3-Jul-2013
Editora: PLOS
Citação: 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. doi:10.1371/journal.pone.0067434
Resumo: 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.
Peer review: yes
URI: http://hdl.handle.net/10400.7/487
DOI: 10.1371/journal.pone.0067434
Versão do Editor: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0067434
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