ArticleScientific reports2018
Unravelling miRNA regulation in yield of rice (Oryza sativa) based on differential network model.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
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- OsmiR5519 regulates grain size and weight and down-regulates sucrose synthase gene RSUS2 in rice (Oryza sativa L.).Planta · 2024Article
- High Daytime Temperature Responsive MicroRNA Profiles in Developing Grains of Rice Varieties with Contrasting Chalkiness.International journal of molecular sciences · 2023Article
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- Landscape dynamic network biomarker analysis reveals the tipping point of transcriptome reprogramming to prevent skin photodamage.Journal of molecular cell biology · 2022Article
- Integration of multi-omics technologies for crop improvement: Status and prospects.Frontiers in bioinformatics · 2022Article
- An Insight Into Pentatricopeptide-Mediated Chloroplast NecrosisFrontiers in genetics · 2022Article
- Non-Coding RNAs in Response to Drought Stress.International journal of molecular sciences · 2021Review
- The Elite Alleles of OsSPL4 Regulate Grain Size and Increase Grain Yield in Rice.Rice (New York, N.Y.) · 2021Article
- Rice miR1432 Fine-Tunes the Balance of Yield and Blast Disease Resistance via Different Modules.Rice (New York, N.Y.) · 2021Article
- MicroRNAs modulate ethylene induced retrograde signal for rice endosperm starch biosynthesis by default expression of transcriptome.Scientific reports · 2021Article
- Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype-Phenotype Association Study.Frontiers in cell and developmental biology · 2021Article
- Identification of Key Genes for the Ultrahigh Yield of Rice Using Dynamic Cross-tissue Network Analysis.Genomics, proteomics & bioinformatics · 2020Article
- Drought Response in Rice: The miRNA Story.International journal of molecular sciences · 2019Review
- Genome-wide analysis of long non-coding RNAs unveils the regulatory roles in the heat tolerance of Chinese cabbage (Brassica rapa ssp.chinensis).Scientific reports · 2019Article
- High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.Frontiers in genetics · 2019Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Rice (Oryza sativa L.) is one of the essential staple food crops and tillering, panicle branching and grain filling are three important traits determining the grain yield. Although miRNAs have been reported being regulating yield, no study has systematically investigated how miRNAs differentially function in high and low yield rice, in particular at a network level. This abundance of data from high-throughput sequencing provides an effective solution for systematic identification of regulatory miRNAs using developed algorithms in plants. We here present a novel algorithm, Gene Co-expression Network differential edge-like transformation (GRN-DET), which can identify key regulatory miRNAs in plant development. Based on the small RNA and RNA-seq data, miRNA-gene-TF co-regulation networks were constructed for yield of rice. Using GRN-DET, the key regulatory miRNAs for rice yield were characterized by the differential expression variances of miRNAs and co-variances of miRNA-mRNA, including osa-miR171 and osa-miR1432. Phytohormone cross-talks (auxin and brassinosteroid) were also revealed by these co-expression networks for the yield of rice.
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