RNAi Projects
This project includes a number of web tools to analyze RNAi off-target effects. While siRNA is generally specific to its target, extensive off-target knockdown effects do occur. To facilitate RNAi designers controlling nonspecific gene silencing, we provide these tools. Please note, computational models only provide reasonable predictions and we are making ongoing improvements to make them better.
- RNAi Target Detection.
This tool searches for potential genes matched by a sequence of siRNA or dsRNA, allowing for 19 and 21 nt of siRNA length, and up to 5 nt mismatches, in H. sapiens , C. elegans, and S. pombe. Rational design scores are also reported.
- siRNA Uniqueness
Unique siRNA occurs only once, whereas the most off-targeting siRNA occurs a few hundred times. This tool returns the number of occurrence in the genome of each siRNA in a given sequence.
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siRNA Off-Target Search(SOS)
SOS uses a hybrid, q-gram based approach, combining two filtering techniques using overlapping and non-overlapping q-grams. The three main improvements over existing methods are: the introduction of a more general cost model (an affine bulge cost model) for siRNA-mRNA off-target alignment; the use of separate searches for alignments with and without bulges, that enables efficient discovery of potential off-target candidates in the filtration phase; and the use of position-preserving and order-preserving hit-processing techniques, that further improves the filtration efficiency.
This approach considers three types of imperfect matches based on biological experiments, namely G:U wobbles, mismatches, and bulges. Given an affine bulge cost model, SOS finds all potential off-targets within the genome which have off-target score equal to or lower than a user-defined threshold. - Gene Family Knockdown
(GFK)
GFK selects an efficient set of siRNAs for RNA interference (RNAi)-based gene family knockdown experiments. It uses a probabilistic greedy algorithm for finding the minimal set of siRNAs that (a) cover a targeted gene family or a specified subset of it, (b) do not cover any untargeted genes, and (c) are individually highly effective at inducing knockdown. In many common cases, this approach significantly reduces the number of siRNAs required in gene family knockdown experiments, as compared to knocking down genes independently. - Efficacy Prediction for siRNA sequences
A key component of RNAi applications is the selection of effective siRNA sequences—ones that are highly functional in degrading more than 80% of the targeted mRNA. The goal of siRNA efficacy prediction is to aid in designing siRNA sequences that are highly efficient in degrading target mRNA sequences. This tool predicts siRNA functionality (knockdown of a certain percentage of mRNA) by using frequently occurring patterns in siRNAs.
More details can be found in our related publications. A quick list of all these tools can be found on the tools page.
