Gutenberg Open Science: Interactive and reproducible workflows for exploring and modeling RNA-seq data with pcaExplorer, ideal, and GeneTonic

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-8609

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DC Field	Value	Language
dc.contributor.author	Ludt, Annekathrin	-
dc.contributor.author	Ustjanzew, Arsenij	-
dc.contributor.author	Binder, Harald	-
dc.contributor.author	Strauch, Konstantin	-
dc.contributor.author	Marini, Federico	-
dc.date.accessioned	2023-01-19T10:34:56Z	-
dc.date.available	2023-01-19T10:34:56Z	-
dc.date.issued	2022	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/8625	-
dc.description.abstract	The generation and interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task. While raw data quality control, alignment, and quantification can be streamlined via efficient algorithms that can deliver the preprocessed expression matrix, a common bottleneck in the analysis of such large datasets is the subsequent in-depth, iterative processes of data exploration, statistical testing, visualization, and interpretation. Specific tools for these workflow steps are available but require a level of technical expertise which might be prohibitive for life and clinical scientists, who are left with essential pieces of information distributed among different tabular and list formats. Our protocols are centered on the joint use of our Bioconductor packages (pcaExplorer, ideal, GeneTonic) for interactive and reproducible workflows. All our packages provide an interactive and accessible experience via Shiny web applications, while still documenting the steps performed with RMarkdown as a framework to guarantee the reproducibility of the analyses, reducing the overall time to generate insights from the data at hand. These protocols guide readers through the essential steps of Exploratory Data Analysis, statistical testing, and functional enrichment analyses, followed by integration and contextualization of results. In our packages, the core elements are linked together in interactive widgets that make drill-down tasks efficient by viewing the data at a level of increased detail. Thanks to their interoperability with essential classes and gold-standard pipelines implemented in the open-source Bioconductor project and community, these protocols will permit complex tasks in RNA-seq data analysis, combining interactivity and reproducibility for following modern best scientific practices and helping to streamline the discovery process for transcriptome data. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.	en_GB
dc.description.sponsorship	Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491381577	de
dc.language.iso	eng	de
dc.rights	CC BY-NC	*
dc.rights.uri	https://creativecommons.org/licenses/by-nc/4.0/	*
dc.subject.ddc	610 Medizin	de_DE
dc.subject.ddc	610 Medical sciences	en_GB
dc.title	Interactive and reproducible workflows for exploring and modeling RNA-seq data with pcaExplorer, ideal, and GeneTonic	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-8609	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 04 Medizin	de
jgu.organisation.number	2700	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Current protocols	de
jgu.journal.volume	2	de
jgu.journal.issue	4	de
jgu.pages.alternative	e411	de
jgu.publisher.year	2022	-
jgu.publisher.name	Wiley	de
jgu.publisher.place	Hoboken, NJ	de
jgu.publisher.issn	2691-1299	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	610	de
jgu.publisher.doi	10.1002/cpz1.411	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Lebenswissenschaften	de
Appears in collections:	DFG-491381577-H

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