CoExp: A Web Tool for the Exploitation of Co-expression Networks

Sonia García-Ruiz; Ana L Gil-Martínez; Alejandro Cisterna; Federico Jurado-Ruiz; Regina H Reynolds; NABEC (North America Brain Expression Consortium); Mark R Cookson; John Hardy; Mina Ryten; Juan A Botía

doi:10.3389/fgene.2021.630187

CoExp: A Web Tool for the Exploitation of Co-expression Networks

Front Genet. 2021 Feb 24:12:630187. doi: 10.3389/fgene.2021.630187. eCollection 2021.

Authors

Sonia García-Ruiz^{1

2

3}, Ana L Gil-Martínez¹, Alejandro Cisterna⁴, Federico Jurado-Ruiz⁴, Regina H Reynolds^{1

2

3}; NABEC (North America Brain Expression Consortium); Mark R Cookson⁵, John Hardy^{6

7

8

9

10}, Mina Ryten^{1

2

3}, Juan A Botía^{3

4}

Affiliations

¹ Institute of Neurology, University College London, London, United Kingdom.
² NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, United Kingdom.
³ Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom.
⁴ Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain.
⁵ Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States.
⁶ Department of Neurodegenerative Diseases, UCL Queen Square Institute of Neurology, London, United Kingdom.
⁷ Department of Neurodegenerative Disease, United Kingdom Dementia Research Institute at UCL, UCL Institute of Neurology, University College London, London, United Kingdom.
⁸ Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, London, United Kingdom.
⁹ UCL Movement Disorders Centre, University College London, London, United Kingdom.
¹⁰ Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong, China.

Abstract

Gene co-expression networks are a powerful type of analysis to construct gene groupings based on transcriptomic profiling. Co-expression networks make it possible to discover modules of genes whose mRNA levels are highly correlated across samples. Subsequent annotation of modules often reveals biological functions and/or evidence of cellular specificity for cell types implicated in the tissue being studied. There are multiple ways to perform such analyses with weighted gene co-expression network analysis (WGCNA) amongst one of the most widely used R packages. While managing a few network models can be done manually, it is often more advantageous to study a wider set of models derived from multiple independently generated transcriptomic data sets (e.g., multiple networks built from many transcriptomic sources). However, there is no software tool available that allows this to be easily achieved. Furthermore, the visual nature of co-expression networks in combination with the coding skills required to explore networks, makes the construction of a web-based platform for their management highly desirable. Here, we present the CoExp Web application, a user-friendly online tool that allows the exploitation of the full collection of 109 co-expression networks provided by the CoExpNets suite of R packages. We describe the usage of CoExp, including its contents and the functionality available through the family of CoExpNets packages. All the tools presented, including the web front- and back-ends are available for the research community so any research group can build its own suite of networks and make them accessible through their own CoExp Web application. Therefore, this paper is of interest to both researchers wishing to annotate their genes of interest across different brain network models and specialists interested in the creation of GCNs looking for a tool to appropriately manage, use, publish, and share their networks in a consistent and productive manner.

Keywords: brain; co-expression network; guilt by association; transcriptomics; web app for neuroscience.

Abstract

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