Gwenaëlle G Lemoine1, Marie-Pier Scott-Boyer2, Bathilde Ambroise3, Olivier Périn3, Arnaud Droit4,5. 1. Département de médecine moléculaire, Faculté de médecine, Université Laval, 2325 rue de l'Université, Québec, G1V 0A6, Canada. 2. Centre de recherche du Chu de Quebec-Université Laval, 2705 boulevard Laurier Québec, Québec, G1V 4G2, Canada. 3. L'Oréal Research and Innovation, 15 rue Pierre Dreyfus, 92110, Clichy, France. 4. Département de médecine moléculaire, Faculté de médecine, Université Laval, 2325 rue de l'Université, Québec, G1V 0A6, Canada. Arnaud.Droit@crchudequebec.ulaval.ca. 5. Centre de recherche du Chu de Quebec-Université Laval, 2705 boulevard Laurier Québec, Québec, G1V 4G2, Canada. Arnaud.Droit@crchudequebec.ulaval.ca.
Abstract
BACKGROUND: Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. An extended description of each of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline. RESULTS: Here we present GWENA, a new R package that integrates gene co-expression network construction and whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performance, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. Remarkably, we prioritized a gene whose involvement was unknown in the muscle development and growth. Moreover, new insights on the variations in patterns of co-expression were identified. The known phenomena of connectivity loss associated with aging was found coupled to a global reorganization of the relationships leading to expression of known aging related functions. CONCLUSION: GWENA is an R package available through Bioconductor ( https://bioconductor.org/packages/release/bioc/html/GWENA.html ) that has been developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as differential co-expression, the package helps to dissect the role of genes relationships in diseases conditions or targeted phenotypes. GWENA goes beyond existing packages that perform co-expression analysis by including new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization.
BACKGROUND: Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. An extended description of each of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline. RESULTS: Here we present GWENA, a new R package that integrates gene co-expression network construction and whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performance, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. Remarkably, we prioritized a gene whose involvement was unknown in the muscle development and growth. Moreover, new insights on the variations in patterns of co-expression were identified. The known phenomena of connectivity loss associated with aging was found coupled to a global reorganization of the relationships leading to expression of known aging related functions. CONCLUSION:GWENA is an R package available through Bioconductor ( https://bioconductor.org/packages/release/bioc/html/GWENA.html ) that has been developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as differential co-expression, the package helps to dissect the role of genes relationships in diseases conditions or targeted phenotypes. GWENA goes beyond existing packages that perform co-expression analysis by including new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization.
Authors: Bin Zhang; Chris Gaiteri; Liviu-Gabriel Bodea; Zhi Wang; Joshua McElwee; Alexei A Podtelezhnikov; Chunsheng Zhang; Tao Xie; Linh Tran; Radu Dobrin; Eugene Fluder; Bruce Clurman; Stacey Melquist; Manikandan Narayanan; Christine Suver; Hardik Shah; Milind Mahajan; Tammy Gillis; Jayalakshmi Mysore; Marcy E MacDonald; John R Lamb; David A Bennett; Cliona Molony; David J Stone; Vilmundur Gudnason; Amanda J Myers; Eric E Schadt; Harald Neumann; Jun Zhu; Valur Emilsson Journal: Cell Date: 2013-04-25 Impact factor: 41.582
Authors: Princy Parsana; Claire Ruberman; Andrew E Jaffe; Michael C Schatz; Alexis Battle; Jeffrey T Leek Journal: Genome Biol Date: 2019-05-16 Impact factor: 13.583
Authors: Ludivine Doridot; Sarah A Hannou; Sarah A Krawczyk; Wenxin Tong; Mi-Sung Kim; Gregory S McElroy; Alan J Fowler; Inna I Astapova; Mark A Herman Journal: Nutrients Date: 2021-10-18 Impact factor: 5.717
Authors: Robert E Paull; Dessireé Zerpa-Catanho; Nancy J Chen; Gail Uruu; Ching Man Jennifer Wai; Michael Kantar Journal: Plant Direct Date: 2022-09-02
Authors: Marie Oestreich; Lisa Holsten; Shobhit Agrawal; Kilian Dahm; Philipp Koch; Han Jin; Matthias Becker; Thomas Ulas Journal: Bioinformatics Date: 2022-10-14 Impact factor: 6.931