Please use this identifier to cite or link to this item: doi:10.22028/D291-36012
Title: miRSwitch: detecting microRNA arm shift and switch events
Author(s): Kern, Fabian
Amand, Jeremy
Senatorov, Ilya
Isakova, Alina
Backes, Christina
Meese, Eckart
Keller, Andreas
Fehlmann, Tobias
Language: English
Title: Nucleic Acids Research
Volume: 48
Issue: W1
Pages: W268–W274
Publisher/Platform: Oxford University Press
Year of Publication: 2020
DDC notations: 610 Medicine and health
Publikation type: Journal Article
Abstract: Arm selection, the preferential expression of a 3′ or 5′ mature microRNA (miRNA), is a highly dynamic and tissue-specific process. Time-dependent expression shifts or switches between the arms are also relevant for human diseases. We present miRSwitch, a web server to facilitate the analysis and interpretation of arm selection events. Our species-independent tool evaluates pre-processed small non-coding RNA sequencing (sncRNA-seq) data, i.e. expression matrices or output files from miRNA quantification tools (miRDeep2, miRMaster, sRNAbench). miRSwitch highlights potential changes in the distribution of mature miRNAs from the same precursor. Group comparisons from one or several user-provided annotations (e.g. disease states) are possible. Results can be dynamically adjusted by choosing from a continuous range of highly specific to very sensitive parameters. Users can compare potential arm shifts in the provided data to a human reference map of pre-computed arm shift frequencies. We created this map from 46 tissues and 30 521 samples. As case studies we present novel arm shift information in a Alzheimer’s disease biomarker data set and from a comparison of tissues in Homo sapiens and Mus musculus. In summary, miRSwitch offers a broad range of customized arm switch analyses along with comprehensive visualizations, and is freely available at: https://www.ccb.uni-saarland.de/mirswitch/.
DOI of the first publication: 10.1093/nar/gkaa323
Link to this record: urn:nbn:de:bsz:291--ds-360129
hdl:20.500.11880/32808
http://dx.doi.org/10.22028/D291-36012
ISSN: 1362-4962
0305-1048
Date of registration: 19-Apr-2022
Faculty: M - Medizinische Fakultät
Department: M - Humangenetik
M - Medizinische Biometrie, Epidemiologie und medizinische Informatik
Professorship: M - Univ.-Prof. Dr. Andreas Keller
M - Prof. Dr. Eckhart Meese
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

Files for this record:
File Description SizeFormat 
gkaa323.pdf1,34 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons