Identifying stress responsive genes using overlapping communities in co-expression networks

Abstract Background This paper proposes a workflow to identify genes that respond to specific treatments in plants. The workflow takes as input the RNA sequencing read counts and phenotypical data of different genotypes, measured under control and treatment conditions. It outputs a reduced group of...

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Autores principales:	Camila Riccio-Rengifo, Jorge Finke, Camilo Rocha
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Stress-responsive genes Co-expression network Overlapping communities Phenotypic traits LASSO Salinity Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/4a22b65359fc44658a31be3ba4f43c55
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spelling	oai:doaj.org-article:4a22b65359fc44658a31be3ba4f43c552021-11-14T12:13:11ZIdentifying stress responsive genes using overlapping communities in co-expression networks10.1186/s12859-021-04462-41471-2105https://doaj.org/article/4a22b65359fc44658a31be3ba4f43c552021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04462-4https://doaj.org/toc/1471-2105Abstract Background This paper proposes a workflow to identify genes that respond to specific treatments in plants. The workflow takes as input the RNA sequencing read counts and phenotypical data of different genotypes, measured under control and treatment conditions. It outputs a reduced group of genes marked as relevant for treatment response. Technically, the proposed approach is both a generalization and an extension of WGCNA. It aims to identify specific modules of overlapping communities underlying the co-expression network of genes. Module detection is achieved by using Hierarchical Link Clustering. The overlapping nature of the systems’ regulatory domains that generate co-expression can be identified by such modules. LASSO regression is employed to analyze phenotypic responses of modules to treatment. Results The workflow is applied to rice (Oryza sativa), a major food source known to be highly sensitive to salt stress. The workflow identifies 19 rice genes that seem relevant in the response to salt stress. They are distributed across 6 modules: 3 modules, each grouping together 3 genes, are associated to shoot K content; 2 modules of 3 genes are associated to shoot biomass; and 1 module of 4 genes is associated to root biomass. These genes represent target genes for the improvement of salinity tolerance in rice. Conclusions A more effective framework to reduce the search-space for target genes that respond to a specific treatment is introduced. It facilitates experimental validation by restraining efforts to a smaller subset of genes of high potential relevance.Camila Riccio-RengifoJorge FinkeCamilo RochaBMCarticleStress-responsive genesCo-expression networkOverlapping communitiesPhenotypic traitsLASSOSalinityComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Stress-responsive genes Co-expression network Overlapping communities Phenotypic traits LASSO Salinity Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Stress-responsive genes Co-expression network Overlapping communities Phenotypic traits LASSO Salinity Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Camila Riccio-Rengifo Jorge Finke Camilo Rocha Identifying stress responsive genes using overlapping communities in co-expression networks
description	Abstract Background This paper proposes a workflow to identify genes that respond to specific treatments in plants. The workflow takes as input the RNA sequencing read counts and phenotypical data of different genotypes, measured under control and treatment conditions. It outputs a reduced group of genes marked as relevant for treatment response. Technically, the proposed approach is both a generalization and an extension of WGCNA. It aims to identify specific modules of overlapping communities underlying the co-expression network of genes. Module detection is achieved by using Hierarchical Link Clustering. The overlapping nature of the systems’ regulatory domains that generate co-expression can be identified by such modules. LASSO regression is employed to analyze phenotypic responses of modules to treatment. Results The workflow is applied to rice (Oryza sativa), a major food source known to be highly sensitive to salt stress. The workflow identifies 19 rice genes that seem relevant in the response to salt stress. They are distributed across 6 modules: 3 modules, each grouping together 3 genes, are associated to shoot K content; 2 modules of 3 genes are associated to shoot biomass; and 1 module of 4 genes is associated to root biomass. These genes represent target genes for the improvement of salinity tolerance in rice. Conclusions A more effective framework to reduce the search-space for target genes that respond to a specific treatment is introduced. It facilitates experimental validation by restraining efforts to a smaller subset of genes of high potential relevance.
format	article
author	Camila Riccio-Rengifo Jorge Finke Camilo Rocha
author_facet	Camila Riccio-Rengifo Jorge Finke Camilo Rocha
author_sort	Camila Riccio-Rengifo
title	Identifying stress responsive genes using overlapping communities in co-expression networks
title_short	Identifying stress responsive genes using overlapping communities in co-expression networks
title_full	Identifying stress responsive genes using overlapping communities in co-expression networks
title_fullStr	Identifying stress responsive genes using overlapping communities in co-expression networks
title_full_unstemmed	Identifying stress responsive genes using overlapping communities in co-expression networks
title_sort	identifying stress responsive genes using overlapping communities in co-expression networks
publisher	BMC
publishDate	2021
url	https://doaj.org/article/4a22b65359fc44658a31be3ba4f43c55
work_keys_str_mv	AT camilaricciorengifo identifyingstressresponsivegenesusingoverlappingcommunitiesincoexpressionnetworks AT jorgefinke identifyingstressresponsivegenesusingoverlappingcommunitiesincoexpressionnetworks AT camilorocha identifyingstressresponsivegenesusingoverlappingcommunitiesincoexpressionnetworks
_version_	1718429343879790592

Identifying stress responsive genes using overlapping communities in co-expression networks

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