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dc.contributor.authorM. Chalk, Alistair
dc.contributor.authorWahlestedt, Claes
dc.contributor.authorL.L. Sonnhammer, Erik
dc.date.accessioned2017-05-03T15:23:17Z
dc.date.available2017-05-03T15:23:17Z
dc.date.issued2004
dc.date.modified2009-01-06T06:39:01Z
dc.identifier.issn10902104
dc.identifier.doi10.1016/j.bbrc.2004.04.181
dc.identifier.urihttp://hdl.handle.net/10072/20943
dc.description.abstractShort interfering RNAs are used in functional genomics studies to knockdown a single gene in a reversible manner. The results of siRNA experiments are highly dependent on the choice of siRNA sequence. In order to evaluate siRNA design rules, we collected a database of 398 siRNAs of known efficacy from 92 genes. We used this database to evaluate previously proposed rules from smaller datasets, and to find a new set of rules that are optimal for the entire database. We also trained a regression tree with full cross-validation. It was however difficult to obtain the same precision as methods previously tested on small datasets from one or two genes. We show that those methods are overfitting as they work poorly on independent validation datasets from multiple genes. Our new design rules can predict siRNAs with efficacy >/= 50% in 91% of cases, and with efficacy >/=90% in 52% of cases, which is more than a twofold improvement over random selection. Software for designing siRNAs is available online via a web server at or as a standalone version for high-throughput applications.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherAcademic Press
dc.publisher.placeUnited States
dc.publisher.urihttp://www.elsevier.com/wps/find/journaldescription.cws_home/622790/description#description
dc.relation.ispartofpagefrom264
dc.relation.ispartofpageto274
dc.relation.ispartofissue1
dc.relation.ispartofjournalBiochemical and Biophysical Research Communications
dc.relation.ispartofvolume319
dc.subject.fieldofresearchMedicinal and Biomolecular Chemistry
dc.subject.fieldofresearchBiochemistry and Cell Biology
dc.subject.fieldofresearchMedical Biochemistry and Metabolomics
dc.subject.fieldofresearchcode0304
dc.subject.fieldofresearchcode0601
dc.subject.fieldofresearchcode1101
dc.titleImproved and automated prediction of effective siRNA.
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.date.issued2004
gro.hasfulltextNo Full Text
gro.griffith.authorChalk, Alistair M.


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