Autoren:	Marini, Federico Ludt, Annekathrin Linke, Jan Strauch, Konstantin
Titel:	GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data
Online-Publikationsdatum:	5-Jul-2022
Erscheinungsdatum:	2021
Sprache des Dokuments:	Englisch
Zusammenfassung/Abstract:	BACKGROUND The interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task, where the essential information is distributed among different tabular and list formats—normalized expression values, results from differential expression analysis, and results from functional enrichment analyses. A number of tools and databases are widely used for the purpose of identification of relevant functional patterns, yet often their contextualization within the data and results at hand is not straightforward, especially if these analytic components are not combined together efficiently. RESULTS We developed the GeneTonic software package, which serves as a comprehensive toolkit for streamlining the interpretation of functional enrichment analyses, by fully leveraging the information of expression values in a differential expression context. GeneTonic is implemented in R and Shiny, leveraging packages that enable HTML-based interactive visualizations for executing drilldown tasks seamlessly, viewing the data at a level of increased detail. GeneTonic is integrated with the core classes of existing Bioconductor workflows, and can accept the output of many widely used tools for pathway analysis, making this approach applicable to a wide range of use cases. Users can effectively navigate interlinked components (otherwise available as flat text or spreadsheet tables), bookmark features of interest during the exploration sessions, and obtain at the end a tailored HTML report, thus combining the benefits of both interactivity and reproducibility. CONCLUSION GeneTonic is distributed as an R package in the Bioconductor project (https://bioconductor.org/packages/GeneTonic/) under the MIT license. Offering both bird’s-eye views of the components of transcriptome data analysis and the detailed inspection of single genes, individual signatures, and their relationships, GeneTonic aims at simplifying the process of interpretation of complex and compelling RNA-seq datasets for many researchers with different expertise profiles.
DDC-Sachgruppe:	610 Medizin 610 Medical sciences
Veröffentlichende Institution:	Johannes Gutenberg-Universität Mainz
Organisationseinheit:	FB 04 Medizin
Veröffentlichungsort:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-7302
Version:	Published version
Publikationstyp:	Zeitschriftenaufsatz
Nutzungsrechte:	CC BY
Informationen zu den Nutzungsrechten:	https://creativecommons.org/licenses/by/4.0/
Zeitschrift:	BMC bioinformatics 22
Seitenzahl oder Artikelnummer:	610
Verlag:	BioMed Central
Verlagsort:	London
Erscheinungsdatum:	2021
ISSN:	1471-2105
DOI der Originalveröffentlichung:	10.1186/s12859-021-04461-5
Enthalten in den Sammlungen:	JGU-Publikationen