DEQOR: a web-based tool for the design and quality control of siRNAs - PubMed (original) (raw)
. 2004 Jul 1;32(Web Server issue):W113-20.
doi: 10.1093/nar/gkh408.
Affiliations
- PMID: 15215362
- PMCID: PMC441546
- DOI: 10.1093/nar/gkh408
DEQOR: a web-based tool for the design and quality control of siRNAs
Andreas Henschel et al. Nucleic Acids Res. 2004.
Abstract
RNA interference (RNAi) is a powerful tool for inhibiting the expression of a gene by mediating the degradation of the corresponding mRNA. The basis of this gene-specific inhibition is small, double-stranded RNAs (dsRNAs), also referred to as small interfering RNAs (siRNAs), that correspond in sequence to a part of the exon sequence of a silenced gene. The selection of siRNAs for a target gene is a crucial step in siRNA-mediated gene silencing. According to present knowledge, siRNAs must fulfill certain properties including sequence length, GC-content and nucleotide composition. Furthermore, the cross-silencing capability of dsRNAs for other genes must be evaluated. When designing siRNAs for chemical synthesis, most of these criteria are achievable by simple sequence analysis of target mRNAs, and the specificity can be evaluated by a single BLAST search against the transcriptome of the studied organism. A different method for raising siRNAs has, however, emerged which uses enzymatic digestion to hydrolyze long pieces of dsRNA into shorter molecules. These endoribonuclease-prepared siRNAs (esiRNAs or 'diced' RNAs) are less variable in their silencing capabilities and circumvent the laborious process of sequence selection for RNAi due to a broader range of products. Though powerful, this method might be more susceptible to cross-silencing genes other than the target itself. We have developed a web-based tool that facilitates the design and quality control of siRNAs for RNAi. The program, DEQOR, uses a scoring system based on state-of-the-art parameters for siRNA design to evaluate the inhibitory potency of siRNAs. DEQOR, therefore, can help to predict (i) regions in a gene that show high silencing capacity based on the base pair composition and (ii) siRNAs with high silencing potential for chemical synthesis. In addition, each siRNA arising from the input query is evaluated for possible cross-silencing activities by performing BLAST searches against the transcriptome or genome of a selected organism. DEQOR can therefore predict the probability that an mRNA fragment will cross-react with other genes in the cell and helps researchers to design experiments to test the specificity of esiRNAs or chemically designed siRNAs. DEQOR is freely available at http://cluster-1.mpi-cbg.de/Deqor/deqor.html.
Figures
Figure 1
Workflow of a DEQOR analysis. First, the input query is submitted to a regular BLASTN search against the selected transcriptome to identify the origin of the input query. Second, the input query is digested in silico into small sequence pieces (between 16 and 25 nt), mimicking siRNAs. Third, each in silico siRNA is submitted to a BLASTN search against the selected transcriptome. Each siRNA is further analyzed for its sequence properties (see text), and disadvantageous sequence features are penalized. Finally, the siRNAs resulting from an input sequence are sorted according to their quality score and their cross-silencing capacities.
Figure 2
Typical output of a DEQOR analysis. The output window is divided into four major parts. (A) A summary section displays the chosen configuration of the DEQOR run, an interactive graphical display of the quality scores of siRNAs along the input sequence, the putative origin of the input sequence and its length and finally a statistical analysis of the overall quality and cross-silencing capacities. (B) The 10 top quality windows are listed, showing their position in the sequence (‘Window’), the sequence of the siRNA (‘Sequence’, with anti-sense strand in boldface), the number of perfect matches (‘Perf. M.’), matches with one mismatch (‘w/1 Mism.’, the percentage of GC–content (‘GC%’), the asymmetric features of the siRNA (‘ASP’; anti-sense preference, with the options ‘yes’, ‘no’ and ‘sym.’), the occurrence of more than three consecutive identical nucleotides in the siRNA sequence (‘Poly’) and finally the quality score of the siRNA (‘Quality’). (C) Windows that are potential cross-silencers are shown. The columns are identical to (B). The accession numbers (NCBI/ENSEMBL) of genes that are targets for cross-silencing activities are listed and linked to their source database at the NCBI/ENSEMBL. Mouse Dip13 α (NM_145221) was used for this DEQOR analysis.
Figure 3
Statistical analysis of esiRNAs from 3500 randomly selected human genes. (A) The % of siRNAs per gene that have a quality score better than 5 (closed circles) and the % siRNAs that meet all standard quality criteria (open circles) were plotted against the % genes. (B) The average quality scores of each input sequence without (dark gray) and with (light gray) cross-silencing capabilities were related to the % genes in the dataset. Quality scores were divided into four categories: (i) below 5; (ii) between 5 and 10; (iii) between 10 and 20; and (iv) above 20. (C) The % high-quality cross-silencers is plotted against the % genes in the dataset. Only 8.49% of genes have one or more cross-silencers with a quality score below 5 (see text for further discussion).
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