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Analysing the meta-interaction between pathways by gene set topological impact analysis

BACKGROUND: Pathway analysis is widely applied in transcriptome analysis. Given certain transcriptomic changes, current pathway analysis tools tend to search for the most impacted pathways, which provides insight into underlying biological mechanisms. Further refining of the enriched pathways and ex...

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Autores principales: Yan, Shen, Chi, Xu, Chang, Xiao, Tian, Mengliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592530/
https://www.ncbi.nlm.nih.gov/pubmed/33109101
http://dx.doi.org/10.1186/s12864-020-07148-y
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author Yan, Shen
Chi, Xu
Chang, Xiao
Tian, Mengliang
author_facet Yan, Shen
Chi, Xu
Chang, Xiao
Tian, Mengliang
author_sort Yan, Shen
collection PubMed
description BACKGROUND: Pathway analysis is widely applied in transcriptome analysis. Given certain transcriptomic changes, current pathway analysis tools tend to search for the most impacted pathways, which provides insight into underlying biological mechanisms. Further refining of the enriched pathways and extracting functional modules by “crosstalk” analysis have been proposed. However, the upstream/downstream relationships between the modules, which may provide extra biological insights such as the coordination of different functional modules and the signal transduction flow have been ignored. RESULTS: To quantitatively analyse the upstream/downstream relationships between functional modules, we developed a novel GEne Set Topological Impact Analysis (GESTIA), which could be used to assemble the enriched pathways and functional modules into a super-module with a topological structure. We showed the advantages of this analysis in the exploration of extra biological insight in addition to the individual enriched pathways and functional modules. CONCLUSIONS: GESTIA can be applied to a broad range of pathway/module analysis result. We hope that GESTIA may help researchers to get one additional step closer to understanding the molecular mechanism from the pathway/module analysis results. SUPPLEMENTARY INFORMATION: Supplementary information accompanies this paper at 10.1186/s12864-020-07148-y.
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spelling pubmed-75925302020-10-29 Analysing the meta-interaction between pathways by gene set topological impact analysis Yan, Shen Chi, Xu Chang, Xiao Tian, Mengliang BMC Genomics Methodology Article BACKGROUND: Pathway analysis is widely applied in transcriptome analysis. Given certain transcriptomic changes, current pathway analysis tools tend to search for the most impacted pathways, which provides insight into underlying biological mechanisms. Further refining of the enriched pathways and extracting functional modules by “crosstalk” analysis have been proposed. However, the upstream/downstream relationships between the modules, which may provide extra biological insights such as the coordination of different functional modules and the signal transduction flow have been ignored. RESULTS: To quantitatively analyse the upstream/downstream relationships between functional modules, we developed a novel GEne Set Topological Impact Analysis (GESTIA), which could be used to assemble the enriched pathways and functional modules into a super-module with a topological structure. We showed the advantages of this analysis in the exploration of extra biological insight in addition to the individual enriched pathways and functional modules. CONCLUSIONS: GESTIA can be applied to a broad range of pathway/module analysis result. We hope that GESTIA may help researchers to get one additional step closer to understanding the molecular mechanism from the pathway/module analysis results. SUPPLEMENTARY INFORMATION: Supplementary information accompanies this paper at 10.1186/s12864-020-07148-y. BioMed Central 2020-10-27 /pmc/articles/PMC7592530/ /pubmed/33109101 http://dx.doi.org/10.1186/s12864-020-07148-y Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology Article
Yan, Shen
Chi, Xu
Chang, Xiao
Tian, Mengliang
Analysing the meta-interaction between pathways by gene set topological impact analysis
title Analysing the meta-interaction between pathways by gene set topological impact analysis
title_full Analysing the meta-interaction between pathways by gene set topological impact analysis
title_fullStr Analysing the meta-interaction between pathways by gene set topological impact analysis
title_full_unstemmed Analysing the meta-interaction between pathways by gene set topological impact analysis
title_short Analysing the meta-interaction between pathways by gene set topological impact analysis
title_sort analysing the meta-interaction between pathways by gene set topological impact analysis
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592530/
https://www.ncbi.nlm.nih.gov/pubmed/33109101
http://dx.doi.org/10.1186/s12864-020-07148-y
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