Improved and automated prediction of effective siRNA

Alistair M. Chalk, Claes R Wahlestedt, Erik L L Sonnhammer

Research output: Contribution to journalArticle

110 Citations (Scopus)

Abstract

Short 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 http://sisearch.cgb.ki.se/ or as a standalone version for high-throughput applications.

Original languageEnglish
Pages (from-to)264-274
Number of pages11
JournalBiochemical and Biophysical Research Communications
Volume319
Issue number1
DOIs
StatePublished - Jun 18 2004
Externally publishedYes

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Small Interfering RNA
Genes
Databases
Genomics
Servers
Software
Throughput
Datasets
Experiments

ASJC Scopus subject areas

  • Biochemistry
  • Biophysics
  • Molecular Biology

Cite this

Improved and automated prediction of effective siRNA. / Chalk, Alistair M.; Wahlestedt, Claes R; Sonnhammer, Erik L L.

In: Biochemical and Biophysical Research Communications, Vol. 319, No. 1, 18.06.2004, p. 264-274.

Research output: Contribution to journalArticle

Chalk, Alistair M. ; Wahlestedt, Claes R ; Sonnhammer, Erik L L. / Improved and automated prediction of effective siRNA. In: Biochemical and Biophysical Research Communications. 2004 ; Vol. 319, No. 1. pp. 264-274.
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