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dc.date.accessioned2021-01-19T20:19:48Z
dc.date.available2021-01-19T20:19:48Z
dc.date.created2021-01-11T14:50:48Z
dc.date.issued2020
dc.identifier.citationKuijjer, Marieke Lydia Fagny, Maud Marin, Alessandro Quackenbush, John Glass, Kimberly . PUMA: PANDA Using MicroRNA Associations. Bioinformatics. 2020
dc.identifier.urihttp://hdl.handle.net/10852/82349
dc.description.abstractAbstract Motivation Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the messenger RNA (mRNA) transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA–target regulatory interactions in the different network models. Furthermore, the tissue-specific functions of miRNAs we identified based on regulatory profiles (available at: https://kuijjer.shinyapps.io/puma_gtex/) are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific miRNA regulatory processes. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue. Availability and implementation PUMA is available in C++, MATLAB and Python on GitHub (https://github.com/kuijjerlab and https://netzoo.github.io/). Supplementary information Supplementary data are available at Bioinformatics online.
dc.languageEN
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titlePUMA: PANDA Using MicroRNA Associations
dc.typeJournal article
dc.creator.authorKuijjer, Marieke Lydia
dc.creator.authorFagny, Maud
dc.creator.authorMarin, Alessandro
dc.creator.authorQuackenbush, John
dc.creator.authorGlass, Kimberly
cristin.unitcode185,57,55,0
cristin.unitnameMarieke Kuijjer Group - Computational Biology and Systems Medicine
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.cristin1869142
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Bioinformatics&rft.volume=&rft.spage=&rft.date=2020
dc.identifier.jtitleBioinformatics
dc.identifier.volume36
dc.identifier.issue18
dc.identifier.startpage4765
dc.identifier.endpage4773
dc.identifier.doihttps://doi.org/10.1093/bioinformatics/btaa571
dc.identifier.urnURN:NBN:no-85244
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn1367-4803
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/82349/2/2020-06-17_kuijjer_bioinformatics.pdf
dc.type.versionPublishedVersion


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