The siRNA-based targeted therapy against fusion gene is emerging as one of the promising strategies for cancer treatment.

This interface design siRNA sequences using a combination of 4 prediction tools namely BiLTR, siDirect, eRNAi and RNAxs. The module first predicts siRNAs for fusion gene sequence by individual tools followed by a comparative report listing siRNA molecules predicted by more than one tools.

Enter sequence

Example   Clear

Select tool for siRNA design

Select    all      none
BiLTR
siDirect
eRNAi
RNAxs




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Set tool specific parameters


BiLTR   
siRNA length:
RNAxs   
Threshold on the 8 nts (seed region) accessibility    
Threshold on the first 16 nts accessibility    
Threshold on the energy assymetry    
Threshold on the sequence assymetry    
Threshold on the self folding energy    
Threshold on the siRNA structure    
Windows size to use for RNAplfold    
Maximal base-pair distance    
siDirect   

Functional siRNA selection algorithm
Ui-Tei et al., Nucleic Acids Res 32, 936-948 (2004)
Reynolds et al., Nat Biotechnol 22, 326-330 (2004)
Amarzguioui et al., BBRC 316, 1050-1058 (2004)

use combined rule:
Ui-Tei + Reynolds + Amarzguioui
Ui-Tei + Reynolds × Amarzguioui
Ui-Tei × Reynolds × Amarzguioui

Seed-duplex stability: Max Tm °C
    (for reducing seed-dependent off-target effect)

Specificity check:  
Hide less-specific siRNAs
Show number of off-target hits within three mismatches

Other options

Target range: from to

Avoid contiguous G's or C's nt or more (for chemically synthesized siRNA)
Avoid contiguous A's or T's nt or more (for shRNA vectors with pol III promoter)

GC content: from % to %

Only show siRNAs that match all checked criteria


eRNAi    

siRNA length for specificity predicition:  
Minimal siRNA efficiency score:
Number of designs reported per query sequence:
Efficiency scoring method:   

Exclude regions of low sequence complexity (mDust)
Exclude >5x CA[ACGT] repeats
Allow relaxation of parameters if required (applies to specificity, efficiency, low-complexity and seed-match options)
Design for non-annotated gene models
Map designs to the genome
Calculate homology of designs to transcripts using e-value cut-off   















































































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