ArticleBMC plant biology2014
ARACNe-based inference, using curated microarray data, of Arabidopsis thaliana root transcriptional regulatory networks.
Article in BMC plant biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- The expression pattern of EXO70 subunits in single-cell stereo-seq of Arabidopsis thaliana leaves using coexistence network analysis.Scientific reports · 2026Article
- The role of DNA content in shaping chromatin architecture and gene expression.The Plant journal : for cell and molecular biology · 2025Article
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- A Common Molecular Signature Indicates the Pre-Meristematic State of Plant Calli.International journal of molecular sciences · 2023Review
- Dissection of canopy layer-specific genetic control of leaf angle in Sorghum bicolor by RNA sequencing.BMC genomics · 2022Article
- Defective cytokinin signaling reprograms lipid and flavonoid gene-to-metabolite networks to mitigate high salinity inProceedings of the National Academy of Sciences of the United States of America · 2021Article
- Overexpression of MADS-box GenePlants (Basel, Switzerland) · 2020Article
- Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling.Frontiers in genetics · 2020Review
- A transcriptional regulatory network of Rsv3-mediated extreme resistance against Soybean mosaic virus.PloS one · 2020Article
- Genome-scale mining of root-preferential genes from maize and characterization of their promoter activity.BMC plant biology · 2019Article
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- Transcriptome-wide analysis of MADS-box family genes involved in aluminum and fluoride assimilation inPlant biotechnology (Tokyo, Japan) · 2018Article
- Gene co-expression analysis for functional classification and gene-disease predictions.Briefings in bioinformatics · 2018Article
- Article
- The MADS-box XAANTAL1 increases proliferation at the Arabidopsis root stem-cell niche and participates in transition to differentiation by regulating cell-cycle components.Annals of botany · 2016Article
- Regulatory network analysis reveals novel regulators of seed desiccation tolerance in Arabidopsis thaliana.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Differential Regulatory Analysis Based on Coexpression Network in Cancer Research.BioMed research international · 2016Review
- OsMADS26 Negatively Regulates Resistance to Pathogens and Drought Tolerance in Rice.Plant physiology · 2015Article
- Arabidopsis ensemble reverse-engineered gene regulatory network discloses interconnected transcription factors in oxidative stress.The Plant cell · 2014Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundUncovering the complex transcriptional regulatory networks (TRNs) that underlie plant and animal development remains a challenge. However, a vast amount of data from public microarray experiments is available, which can be subject to inference algorithms in order to recover reliable TRN architectures.
resultsIn this study we present a simple bioinformatics methodology that uses public, carefully curated microarray data and the mutual information algorithm ARACNe in order to obtain a database of transcriptional interactions. We used data from Arabidopsis thaliana root samples to show that the transcriptional regulatory networks derived from this database successfully recover previously identified root transcriptional modules and to propose new transcription factors for the SHORT ROOT/SCARECROW and PLETHORA pathways. We further show that these networks are a powerful tool to integrate and analyze high-throughput expression data, as exemplified by our analysis of a SHORT ROOT induction time-course microarray dataset, and are a reliable source for the prediction of novel root gene functions. In particular, we used our database to predict novel genes involved in root secondary cell-wall synthesis and identified the MADS-box TF XAL1/AGL12 as an unexpected participant in this process.
conclusionsThis study demonstrates that network inference using carefully curated microarray data yields reliable TRN architectures. In contrast to previous efforts to obtain root TRNs, that have focused on particular functional modules or tissues, our root transcriptional interactions provide an overview of the transcriptional pathways present in Arabidopsis thaliana roots and will likely yield a plethora of novel hypotheses to be tested experimentally.
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