Predicting siRNA Off-Targets

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 How can you predict possible off targets for a given siRNA?


siRNA (short interfering RNA) are small RNA molecules that can be used to silence or knockdown gene expression by binding to target messenger RNA (mRNA) and inducing its degradation. However, in addition to targeting the intended mRNA, siRNA can also bind to off-target mRNAs, leading to unintended effects.


To predict possible off-targets for a given siRNA, several computational tools and databases are available, including:


BLAST (Basic Local Alignment Search Tool): BLAST is a tool that allows you to compare a query sequence against a database of known sequences. By using BLAST, you can compare the siRNA sequence against a database of all known human genes and identify potential off-targets based on sequence similarity.


TargetScan: TargetScan is a software tool that predicts the potential targets of miRNAs (microRNAs) and siRNAs by identifying conserved complementary sites within the 3’ untranslated region (UTR) of mRNA. TargetScan uses sequence and structural features of the mRNA and miRNA/siRNA to predict potential targets, and ranks them based on their context score.


RNA22: RNA22 is a program that identifies potential microRNA binding sites and siRNA off-targets based on sequence complementarity. RNA22 uses an algorithm that takes into account the conservation of binding sites and the position of mismatches between the siRNA and the mRNA.


Off-Spotter: Off-Spotter is a tool that predicts potential off-targets based on the siRNA sequence and mRNA sequence. The tool uses a database of all known human mRNA sequences to identify potential off-targets and ranks them based on their binding energy.


CRISPR-ERA: CRISPR-ERA is a tool that uses machine learning to predict the off-target effects of siRNA and CRISPR gene-editing techniques. CRISPR-ERA takes into account sequence and structural features of the siRNA and mRNA to predict potential off-targets and rank them based on their likelihood of causing off-target effects.


It's important to note that these computational tools are not perfect and may have false positives or false negatives. Therefore, it's always recommended to validate the predicted off-targets experimentally before drawing any conclusions about the siRNA specificity.

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