ArticleFrontiers in plant science2022
Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 9 citations in OpenAlex.
- Predicting transcriptional regulators in plants in the era of artificial intelligence.Current opinion in plant biology · 2026Review
- Article
- Beyond dormancy: organ-specific gene regulatory networks control winter development in peach buds.Horticulture research · 2026Article
- Shoot transcriptome and co-expression network analysis of African rice cultivars identify drought-tolerance hub genes and growth stage-dependent trade-offs.Frontiers in plant science · 2026Article
- Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency.The Plant cell · 2025Article
- A network-enabled pipeline for gene discovery and validation in non-model plant species.Cell reports methods · 2025Article
- Nitrogen sensing and regulatory networks: it's about time and space.The Plant cell · 2024Review
- An integrated transcriptome and physiological analysis of nitrogen use efficiency in rice (Frontiers in genetics · 2024Article
Corrections and comments
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Authors and funding
10 authors at 7 institutions in 4 countries.
Funding
Abstract
Nitrogen (N) and Water (W) - two resources critical for crop productivity - are becoming increasingly limited in soils globally. To address this issue, we aim to uncover the gene regulatory networks (GRNs) that regulate nitrogen use efficiency (NUE) - as a function of water availability - in Oryza sativa, a staple for 3.5 billion people. In this study, we infer and validate GRNs that correlate with rice NUE phenotypes affected by N-by-W availability in the field. We did this by exploiting RNA-seq and crop phenotype data from 19 rice varieties grown in a 2x2 N-by-W matrix in the field. First, to identify gene-to-NUE field phenotypes, we analyzed these datasets using weighted gene co-expression network analysis (WGCNA). This identified two network modules ("skyblue" & "grey60") highly correlated with NUE grain yield (NUEg). Next, we focused on 90 TFs contained in these two NUEg modules and predicted their genome-wide targets using the N-and/or-W response datasets using a random forest network inference approach (GENIE3). Next, to validate the GENIE3 TF→target gene predictions, we performed Precision/Recall Analysis (AUPR) using nine datasets for three TFs validated
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Registered trials
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