ArticleGenome research2006
Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine.
Article in Genome research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers.
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Who cites it
112 citing papers in PubMed, 248 citations in OpenAlex.
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- Restriction of cytosolic sucrose hydrolysis profoundly alters development, metabolism, and gene expression in Arabidopsis roots.Journal of experimental botany · 2021Article
- Interplay between Abscisic Acid and Gibberellins, as Related to Ethylene and Sugars, in Regulating Maturation of Non-Climacteric Fruit.International journal of molecular sciences · 2021Review
- Population Evolution, Genetic Diversity and Structure of the Medicinal Legume,Frontiers in plant science · 2021Article
- Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure.Nature communications · 2020Article
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- GSM2, a transaldolase, contributes to reactive oxygen species homeostasis in Arabidopsis.Plant molecular biology · 2020Article
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- Sugar Transporter ZjSWEET2.2 Mediates Sugar Loading in Leaves ofFrontiers in plant science · 2020Article
52 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Establishing transcriptional regulatory networks by analysis of gene expression data and promoter sequences shows great promise. We developed a novel promoter classification method using a Relevance Vector Machine (RVM) and Bayesian statistical principles to identify discriminatory features in the promoter sequences of genes that can correctly classify transcriptional responses. The method was applied to microarray data obtained from Arabidopsis seedlings treated with glucose or abscisic acid (ABA). Of those genes showing >2.5-fold changes in expression level, approximately 70% were correctly predicted as being up- or down-regulated (under 10-fold cross-validation), based on the presence or absence of a small set of discriminative promoter motifs. Many of these motifs have known regulatory functions in sugar- and ABA-mediated gene expression. One promoter motif that was not known to be involved in glucose-responsive gene expression was identified as the strongest classifier of glucose-up-regulated gene expression. We show it confers glucose-responsive gene expression in conjunction with another promoter motif, thus validating the classification method. We were able to establish a detailed model of glucose and ABA transcriptional regulatory networks and their interactions, which will help us to understand the mechanisms linking metabolism with growth in Arabidopsis. This study shows that machine learning strategies coupled to Bayesian statistical methods hold significant promise for identifying functionally significant promoter sequences.
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