Home > Publications database > Challenges in RNA Regulation in Huntington's Disease: Insights from Computational Studies > print

001     877574
005     20240625095111.0
024 7 _ |a 10.1002/ijch.202000021
|2 doi
024 7 _ |a 0021-2148
|2 ISSN
024 7 _ |a 1869-5868
|2 ISSN
024 7 _ |a 2128/25473
|2 Handle
024 7 _ |a WOS:000540305000001
|2 WOS
037 _ _ |a FZJ-2020-02299
082 _ _ |a 540
100 1 _ |a Palomino, Oscar
|0 P:(DE-Juel1)174273
|b 0
|u fzj
245 _ _ |a Challenges in RNA Regulation in Huntington's Disease: Insights from Computational Studies
260 _ _ |a Weinheim
|c 2020
|b Wiley-VCH
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1600958218_28180
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Novel therapeutic approaches are being developed to tackle neurodegenerative diseases, due to the lack of efficiency of the known druggable targets. For Huntington's disease, a promising approach is the regulation of the RNA product. This target would allow for a selective and effective inhibition of the toxic effects exerted by the final nucleic product and the coded protein. In this review, the current state of the art of RNA regulation is discussed, with a brief but insightful view on novel plausible targets. After this, an emphasis on successful computational and experimental approaches tailored in modeling and regulating RNA aberrant behavior are extensively presented. Finally, the application and limitations of current computational methods are discussed, and possible avenues for improvement are outlined.
536 _ _ |a 572 - (Dys-)function and Plasticity (POF3-572)
|0 G:(DE-HGF)POF3-572
|c POF3-572
|f POF III
|x 0
536 _ _ |a 511 - Computational Science and Mathematical Methods (POF3-511)
|0 G:(DE-HGF)POF3-511
|c POF3-511
|f POF III
|x 1
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Margreiter, Michael A.
|0 P:(DE-Juel1)165176
|b 1
|u fzj
700 1 _ |a Rossetti, Giulia
|0 P:(DE-Juel1)145921
|b 2
|e Corresponding author
|u fzj
773 _ _ |a 10.1002/ijch.202000021
|0 PERI:(DE-600)2066481-3
|n 7
|p 681-693
|t Israel journal of chemistry
|v 60
|y 2020
|x 0021-2148
856 4 _ |u https://juser.fz-juelich.de/record/877574/files/ijch.202000021-1.pdf
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/877574/files/ijch.202000021-1.pdf?subformat=pdfa
|x pdfa
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:877574
|p openaire
|p open_access
|p OpenAPC_DEAL
|p driver
|p VDB
|p openCost
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)174273
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)165176
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)145921
913 1 _ |a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|1 G:(DE-HGF)POF3-570
|0 G:(DE-HGF)POF3-572
|2 G:(DE-HGF)POF3-500
|v (Dys-)function and Plasticity
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
913 1 _ |a DE-HGF
|b Key Technologies
|1 G:(DE-HGF)POF3-510
|0 G:(DE-HGF)POF3-511
|2 G:(DE-HGF)POF3-500
|v Computational Science and Mathematical Methods
|x 1
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|l Supercomputing & Big Data
914 1 _ |y 2020
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2020-02-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2020-02-26
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2020-02-26
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b ISR J CHEM : 2018
|d 2020-02-26
915 _ _ |a DEAL Wiley
|0 StatID:(DE-HGF)3001
|2 StatID
|d 2020-02-26
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2020-02-26
915 _ _ |a WoS
|0 StatID:(DE-HGF)0110
|2 StatID
|b Science Citation Index
|d 2020-02-26
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
|d 2020-02-26
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2020-02-26
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2020-02-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2020-02-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0310
|2 StatID
|b NCBI Molecular Biology Database
|d 2020-02-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2020-02-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2020-02-26
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IAS-5-20120330
|k IAS-5
|l Computational Biomedicine
|x 0
920 1 _ |0 I:(DE-Juel1)INM-9-20140121
|k INM-9
|l Computational Biomedicine
|x 1
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 2
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)IAS-5-20120330
980 _ _ |a I:(DE-Juel1)INM-9-20140121
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a APC
980 _ _ |a UNRESTRICTED
980 1 _ |a APC
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21