Literature DB >> 35895209

Gene Co-expression Network Analysis and Linking Modules to Phenotyping Response in Plants.

Qian Du1, Malachy T Campbell2, Huihui Yu1, Kan Liu1, Harkamal Walia2, Qi Zhang3, Chi Zhang4.   

Abstract

Environmental factors, including different stresses, can have an impact on the expression of genes and subsequently the phenotype and development of plants. Since a large number of genes are involved in response to the perturbation of the environment, identifying groups of co-expressed genes is meaningful. The gene co-expression network models can be used for the exploration, interpretation, and identification of genes responding to environmental changes. Once a gene co-expression network is constructed, one can determine gene modules and the association of gene modules to the phenotypic response. To link modules to phenotype, one approach is to find the correlated eigengenes of given modules or to integrate all eigengenes in regularized linear model. This manuscript describes the method from construction of co-expression network, module discovery, association between modules and phenotypic data, and finally to annotation/visualization.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Association between co-expression network and phenotyping data; Data integration; Gene co-expression network; Module discovery

Mesh:

Year:  2022        PMID: 35895209     DOI: 10.1007/978-1-0716-2537-8_20

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  33 in total

1.  An empirical Bayes approach to inferring large-scale gene association networks.

Authors:  Juliane Schäfer; Korbinian Strimmer
Journal:  Bioinformatics       Date:  2004-10-12       Impact factor: 6.937

Review 2.  Comparative co-expression analysis in plant biology.

Authors:  Sara Movahedi; Michiel Van Bel; Ken S Heyndrickx; Klaas Vandepoele
Journal:  Plant Cell Environ       Date:  2012-05-10       Impact factor: 7.228

3.  The association of multiple interacting genes with specific phenotypes in rice using gene coexpression networks.

Authors:  Stephen P Ficklin; Feng Luo; F Alex Feltus
Journal:  Plant Physiol       Date:  2010-07-28       Impact factor: 8.340

4.  Co-expression of cell-wall related genes: new tools and insights.

Authors:  Colin Ruprecht; Staffan Persson
Journal:  Front Plant Sci       Date:  2012-05-03       Impact factor: 5.753

5.  Building gene co-expression networks using transcriptomics data for systems biology investigations: Comparison of methods using microarray data.

Authors:  Haja N Kadarmideen; Nathan S Watson-Haigh
Journal:  Bioinformation       Date:  2012-09-21

6.  Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types.

Authors:  Yang Yang; Leng Han; Yuan Yuan; Jun Li; Nainan Hei; Han Liang
Journal:  Nat Commun       Date:  2014       Impact factor: 14.919

7.  The structure of a gene co-expression network reveals biological functions underlying eQTLs.

Authors:  Nathalie Villa-Vialaneix; Laurence Liaubet; Thibault Laurent; Pierre Cherel; Adrien Gamot; Magali SanCristobal
Journal:  PLoS One       Date:  2013-04-05       Impact factor: 3.240

8.  Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering.

Authors:  Chuan Gao; Ian C McDowell; Shiwen Zhao; Christopher D Brown; Barbara E Engelhardt
Journal:  PLoS Comput Biol       Date:  2016-07-28       Impact factor: 4.475

9.  Co-expression network analysis of the transcriptomes of rice roots exposed to various cadmium stresses reveals universal cadmium-responsive genes.

Authors:  Mingpu Tan; Dan Cheng; Yuening Yang; Guoqiang Zhang; Mengjie Qin; Jun Chen; Yahua Chen; Mingyi Jiang
Journal:  BMC Plant Biol       Date:  2017-11-07       Impact factor: 4.215

10.  CorSig: a general framework for estimating statistical significance of correlation and its application to gene co-expression analysis.

Authors:  Hong-Qiang Wang; Chung-Jui Tsai
Journal:  PLoS One       Date:  2013-10-23       Impact factor: 3.240

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