RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis [version 2; peer review: 1 approved, 2 approved with reservations]

RNA sequencing (RNA-seq) is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-ser...

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Autores principales:	Margaux Haering, Bianca H Habermann
Formato:	article
Lenguaje:	EN
Publicado:	F1000 Research Ltd 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/55139f6344c94d33a904b20afb77c866
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spelling	oai:doaj.org-article:55139f6344c94d33a904b20afb77c8662021-11-15T15:26:11ZRNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis [version 2; peer review: 1 approved, 2 approved with reservations]2046-140210.12688/f1000research.54533.2https://doaj.org/article/55139f6344c94d33a904b20afb77c8662021-11-01T00:00:00Zhttps://f1000research.com/articles/10-654/v2https://doaj.org/toc/2046-1402RNA sequencing (RNA-seq) is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species. With RNfuzzyApp, we provide a user-friendly, web-based R shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, fully automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, cluster overlap analysis, Mfuzz loop computations, as well as cluster enrichments. RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its assignment of orthologs, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.Margaux HaeringBianca H HabermannF1000 Research LtdarticleMedicineRScienceQENF1000Research, Vol 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Margaux Haering Bianca H Habermann RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis [version 2; peer review: 1 approved, 2 approved with reservations]
description	RNA sequencing (RNA-seq) is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species. With RNfuzzyApp, we provide a user-friendly, web-based R shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, fully automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, cluster overlap analysis, Mfuzz loop computations, as well as cluster enrichments. RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its assignment of orthologs, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.
format	article
author	Margaux Haering Bianca H Habermann
author_facet	Margaux Haering Bianca H Habermann
author_sort	Margaux Haering
title	RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis [version 2; peer review: 1 approved, 2 approved with reservations]
title_short	RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis [version 2; peer review: 1 approved, 2 approved with reservations]
title_full	RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis [version 2; peer review: 1 approved, 2 approved with reservations]
title_fullStr	RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis [version 2; peer review: 1 approved, 2 approved with reservations]
title_full_unstemmed	RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis [version 2; peer review: 1 approved, 2 approved with reservations]
title_sort	rnfuzzyapp: an r shiny rna-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis [version 2; peer review: 1 approved, 2 approved with reservations]
publisher	F1000 Research Ltd
publishDate	2021
url	https://doaj.org/article/55139f6344c94d33a904b20afb77c866
work_keys_str_mv	AT margauxhaering rnfuzzyappanrshinyrnaseqdataanalysisappforvisualisationdifferentialexpressionanalysistimeseriesclusteringandenrichmentanalysisversion2peerreview1approved2approvedwithreservations AT biancahhabermann rnfuzzyappanrshinyrnaseqdataanalysisappforvisualisationdifferentialexpressionanalysistimeseriesclusteringandenrichmentanalysisversion2peerreview1approved2approvedwithreservations
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RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis [version 2; peer review: 1 approved, 2 approved with reservations]

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