An Introduction to Network Analysis in Plant Biology

This beginner's guide is intended for plant biologists new to network analysis. Here, we introduce key concepts and resources for researchers interested in incorporating network analysis into research, either as a stand-alone component for generating hypotheses or as a framework for examining and visualizing experimental results. Network analysis provides a powerful tool to predict gene functions. Advances in and reduced costs for systems biology techniques, such as genomics, transcriptomics, and proteomics, have generated abundant omics data for plants; however, the functional annotation of plant genes lags. Therefore, predictions from network analysis can be a starting point to annotate genes and ultimately elucidate genotype-phenotype relationships. In this paper, we introduce networks and compare network-building resources available for plant biologists, including databases and software for network analysis. We then compare four databases available for plant biologists in more detail: AraNet, GeneMANIA, ATTED-II, and STRING. AraNet and GeneMANIA are functional association networks, ATTED-II is a gene coexpression database, and STRING is a protein-protein interaction database. AraNet and ATTED-II are plant-specific databases that can analyze multiple plant species, whereas GeneMANIA builds networks for <i>Arabidopsis thaliana</i> and nonplant species and STRING for multiple species. Finally, we compare the performance of the four databases in predicting known and probable gene functions of the <i>A. thaliana</i> Nuclear Factor-Y (NF-Y) genes. We conclude that plant biologists have an invaluable resource in these databases and discuss how users can decide which type of database to use depending on their research question.

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Publication Details

Journal
Plant Direct
Published
2025-10-01
DOI
https://doi.org/10.1002/pld3.70114
Citations
1
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
0.81

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An Introduction to Network Analysis in Plant Biology

Chamindika L. Siriwardana, Laura Bartley, Ashleigh S. Carlton, Thalia Lizeth Moncayo et al.
1 citations
Plant Direct
Bioinformatics and Genomic Networks
0.81
article

An Introduction to Network Analysis in Plant Biology

Chamindika L. Siriwardana, Laura Bartley, Ashleigh S. Carlton, Thalia Lizeth Moncayo, Elizabeth A. O’Bier
article en
1 citations

Abstract

This beginner's guide is intended for plant biologists new to network analysis. Here, we introduce key concepts and resources for researchers interested in incorporating network analysis into research, either as a stand-alone component for generating hypotheses or as a framework for examining and visualizing experimental results. Network analysis provides a powerful tool to predict gene functions. Advances in and reduced costs for systems biology techniques, such as genomics, transcriptomics, and proteomics, have generated abundant omics data for plants; however, the functional annotation of plant genes lags. Therefore, predictions from network analysis can be a starting point to annotate genes and ultimately elucidate genotype-phenotype relationships. In this paper, we introduce networks and compare network-building resources available for plant biologists, including databases and software for network analysis. We then compare four databases available for plant biologists in more detail: AraNet, GeneMANIA, ATTED-II, and STRING. AraNet and GeneMANIA are functional association networks, ATTED-II is a gene coexpression database, and STRING is a protein-protein interaction database. AraNet and ATTED-II are plant-specific databases that can analyze multiple plant species, whereas GeneMANIA builds networks for <i>Arabidopsis thaliana</i> and nonplant species and STRING for multiple species. Finally, we compare the performance of the four databases in predicting known and probable gene functions of the <i>A. thaliana</i> Nuclear Factor-Y (NF-Y) genes. We conclude that plant biologists have an invaluable resource in these databases and discuss how users can decide which type of database to use depending on their research question.

Plant DirectVol. 9(10)
Tokyo Electron (Japan) (JP), Washington State University (US), Texas A&M University – Central Texas (US)
National Science Foundation, U.S. Department of Agriculture, National Institute of Food and Agriculture
Openalex Percentile: Top 22%
Bioinformatics and Genomic Networks
0.81
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