Literature DB >> 15591357

Highly specific and accurate selection of siRNAs for high-throughput functional assays.

Javier Santoyo1, Juan M Vaquerizas, Joaquín Dopazo.   

Abstract

MOTIVATION: Small interfering RNA (siRNA) is widely used in functional genomics to silence genes by decreasing their expression to study the resulting phenotypes. The possibility of performing large-scale functional assays by gene silencing accentuates the necessity of a software capable of the high-throughput design of highly specific siRNA. The main objective sought was the design of a large number of siRNAs with appropriate thermodynamic properties and, especially, high specificity. Since all the available procedures require, to some extent, manual processing of the results to guarantee specific results, specificity constitutes to date, the major obstacle to the complete automation of all the steps necessary for the selection of optimal candidate siRNAs. RESULT: Here, we present a program that for the first time completely automates the search for siRNAs. In SiDE, the most complete set of rules for the selection of siRNA candidates (including G+C content, nucleotides at determined positions, thermodynamic properties, propensity to form internal hairpins, etc.) is implemented and moreover, specificity is achieved by a conceptually new method. After selecting possible siRNA candidates with the optimal functional properties, putative unspecific matches, which can cause cross-hybridization, are checked in databases containing a unique entry for each gene. These truly non-redundant databases are constructed from the genome annotations (Ensembl). Also intron/exon boundaries, presence of polymorphisms (single nucleotide polymorphisms) specificity for either gene or transcript, and other features can be selected to be considered in the design of siRNAs. AVAILABILITY: The program is available as a web server at http://side.bioinfo.cnio.es. The program was written under the GPL license. CONTACT: jdopazo@cnio.es.

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Year:  2004        PMID: 15591357     DOI: 10.1093/bioinformatics/bti196

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  12 in total

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5.  A multivariate prediction model for microarray cross-hybridization.

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