Literature DB >> 31436787

Co-expression networks for plant biology: why and how.

Xiaolan Rao1, Richard A Dixon1.   

Abstract

Co-expression network analysis is one of the most powerful approaches for interpretation of large transcriptomic datasets. It enables characterization of modules of co-expressed genes that may share biological functional linkages. Such networks provide an initial way to explore functional associations from gene expression profiling and can be applied to various aspects of plant biology. This review presents the applications of co-expression network analysis in plant biology and addresses optimized strategies from the recent literature for performing co-expression analysis on plant biological systems. Additionally, we describe the combined interpretation of co-expression analysis with other genomic data to enhance the generation of biologically relevant information.
© The Author(s) 2019. Published by Oxford University Press on behalf of the Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Keywords:  co-expression; co-expression network; data mining; gene network; transcriptomics

Mesh:

Substances:

Year:  2019        PMID: 31436787     DOI: 10.1093/abbs/gmz080

Source DB:  PubMed          Journal:  Acta Biochim Biophys Sin (Shanghai)        ISSN: 1672-9145            Impact factor:   3.848


  11 in total

Review 1.  Using Gene Expression to Study Specialized Metabolism-A Practical Guide.

Authors:  Riccardo Delli-Ponti; Devendra Shivhare; Marek Mutwil
Journal:  Front Plant Sci       Date:  2021-01-12       Impact factor: 5.753

2.  Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat.

Authors:  Jun Wei; Yu Fang; Hao Jiang; Xing-Ting Wu; Jing-Hong Zuo; Xian-Chun Xia; Jin-Quan Li; Benjamin Stich; Hong Cao; Yong-Xiu Liu
Journal:  BMC Plant Biol       Date:  2022-06-13       Impact factor: 5.260

Review 3.  Exploiting plant transcriptomic databases: Resources, tools, and approaches.

Authors:  Peng Ken Lim; Xinghai Zheng; Jong Ching Goh; Marek Mutwil
Journal:  Plant Commun       Date:  2022-04-09

4.  Use of a graph neural network to the weighted gene co-expression network analysis of Korean native cattle.

Authors:  Yeong Jun Koh; Seung Hwan Lee; Hyo-Jun Lee; Yoonji Chung; Ki Yong Chung; Young-Kuk Kim; Jun Heon Lee
Journal:  Sci Rep       Date:  2022-06-14       Impact factor: 4.996

5.  Host-Pathogen Responses to Pandemic Influenza H1N1pdm09 in a Human Respiratory Airway Model.

Authors:  Elizabeth A Pharo; Sinéad M Williams; Victoria Boyd; Vinod Sundaramoorthy; Peter A Durr; Michelle L Baker
Journal:  Viruses       Date:  2020-06-24       Impact factor: 5.048

6.  Response of phytohormone mediated plant homeodomain (PHD) family to abiotic stress in upland cotton (Gossypium hirsutum spp.).

Authors:  Huanhuan Wu; Lei Zheng; Ghulam Qanmber; Mengzhen Guo; Zhi Wang; Zuoren Yang
Journal:  BMC Plant Biol       Date:  2021-01-06       Impact factor: 4.215

7.  Co-expression Networks From Gene Expression Variability Between Genetically Identical Seedlings Can Reveal Novel Regulatory Relationships.

Authors:  Sandra Cortijo; Marcel Bhattarai; James C W Locke; Sebastian E Ahnert
Journal:  Front Plant Sci       Date:  2020-12-15       Impact factor: 5.753

8.  Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches.

Authors:  Felipe Roberto Francisco; Alexandre Hild Aono; Carla Cristina da Silva; Paulo S Gonçalves; Erivaldo J Scaloppi Junior; Vincent Le Guen; Roberto Fritsche-Neto; Livia Moura Souza; Anete Pereira de Souza
Journal:  Front Plant Sci       Date:  2021-12-21       Impact factor: 5.753

Review 9.  Gene Co-Expression Network Tools and Databases for Crop Improvement.

Authors:  Rabiatul-Adawiah Zainal-Abidin; Sarahani Harun; Vinothienii Vengatharajuloo; Amin-Asyraf Tamizi; Nurul Hidayah Samsulrizal
Journal:  Plants (Basel)       Date:  2022-06-21

10.  Presence of a Mitovirus Is Associated with Alteration of the Mitochondrial Proteome, as Revealed by Protein-Protein Interaction (PPI) and Co-Expression Network Models in Chenopodium quinoa Plants.

Authors:  Dario Di Silvestre; Giulia Passignani; Rossana Rossi; Marina Ciuffo; Massimo Turina; Gianpiero Vigani; Pier Luigi Mauri
Journal:  Biology (Basel)       Date:  2022-01-08
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