ArticleHorticulture research2014
Construction and validation of a gene co-expression network in grapevine (Vitis vinifera. L.).
Article in Horticulture research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 39 citations in OpenAlex.
- Data-Mining of Barley to Identify Salt Stress Hub Genes, Gene Expression Analysis and Recombinant Plasmid Construction.Iranian journal of biotechnology · 2023Article
- Mining Unknown Porcine Protein Isoforms by Tissue-based Map of Proteome Enhances Pig Genome Annotation.Genomics, proteomics & bioinformatics · 2021Article
- Weighted gene co-expression network analysis unveils gene networks regulating folate biosynthesis in maize endosperm.3 Biotech · 2021Article
- Global identification of long non-coding RNAs involved in the induction of spinach flowering.BMC genomics · 2021Article
- Modern Approaches for Transcriptome Analyses in Plants.Advances in experimental medicine and biology · 2021Article
- Whitefly Network Analysis Reveals Gene Modules Involved in Host Plant Selection, Development and Evolution.Frontiers in physiology · 2021Article
- Transcriptional regulation of bark freezing tolerance in apple (Malus domestica Borkh.).Horticulture research · 2020Article
- WGCNA Analysis of Salt-Responsive Core Transcriptome Identifies Novel Hub Genes in Rice.Genes · 2019Article
- Analysis of chickpea gene co-expression networks and pathways during heavy metal stress.Journal of biosciences · 2019Article
- Identification of putative drought-responsive genes in rice using gene co-expression analysis.Bioinformation · 2019Article
- The grapevine kinome: annotation, classification and expression patterns in developmental processes and stress responses.Horticulture research · 2018Article
- Article
- Analysis of weighted co-regulatory networks in maize provides insights into new genes and regulatory mechanisms related to inositol phosphate metabolism.BMC genomics · 2016Article
- Identification and Network-Enabled Characterization of Auxin Response Factor Genes inFrontiers in plant science · 2016Article
- Characterization of CIPK Family in Asian Pear (Pyrus bretschneideri Rehd) and Co-expression Analysis Related to Salt and Osmotic Stress Responses.Frontiers in plant science · 2016Article
- Differential Network Analysis Reveals Evolutionary Complexity in Secondary Metabolism of Rauvolfia serpentina over Catharanthus roseus.Frontiers in plant science · 2016Article
- Construction of citrus gene coexpression networks from microarray data using random matrix theory.Horticulture research · 2015Article
- Functional characterization of drought-responsive modules and genes in Oryza sativa: a network-based approach.Frontiers in genetics · 2015Article
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene co-expression analysis has been widely used for predicting gene functions because genes within modules of a co-expression network may be involved in similar biological processes and exhibit similar biological functions. To detect gene relationships in the grapevine genome, we constructed a grapevine gene co-expression network (GGCN) by compiling a total of 374 publically available grapevine microarray datasets. The GGCN consisted of 557 modules containing a total of 3834 nodes with 13 479 edges. The functions of the subnetwork modules were inferred by Gene ontology (GO) enrichment analysis. In 127 of the 557 modules containing two or more GO terms, 38 modules exhibited the most significantly enriched GO terms, including 'protein catabolism process', 'photosynthesis', 'cell biosynthesis process', 'biosynthesis of plant cell wall', 'stress response' and other important biological processes. The 'response to heat' GO term was highly represented in module 17, which is composed of many heat shock proteins. To further determine the potential functions of genes in module 17, we performed a Pearson correlation coefficient test, analyzed orthologous relationships with Arabidopsis genes and established gene expression correlations with real-time quantitative reverse transcriptase PCR (qRT-PCR). Our results indicated that many genes in module 17 were upregulated during the heat shock and recovery processes and downregulated in response to low temperature. Furthermore, two putative genes, Vit_07s0185g00040 and Vit_02s0025g04060, were highly expressed in response to heat shock and recovery. This study provides insight into GGCN gene modules and offers important references for gene functions and the discovery of new genes at the module level.
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Registered trials
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