Low hit validation rate for Dharmacon siGENOME screens

Low hit validation rate for Dharmacon siGENOME screens

Good experimental design is important when validating hits from RNAi screens.  Off-target effects from single siRNAs and low-complexity siRNA pools (e.g. Dharmacon siGENOME) result in high false-positive rates that must be sorted out in validation experiments. Dharmacon siGENOME pools (SMARTpools) have 4 siRNAs, and the most common form of validation is to test the pool siRNAs individually (deconvolution). Unfortunately, the results of such deconvolution screening rounds are difficult to interpret. The pool phenotype could be due to the off-target effects of…

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Clearly compensating

Clearly compensating

Genetic compensation by transcriptional adaptation is a process whereby knocking out a gene (e.g by CRISPR or TALEN) results in the deregulation of genes that make up for the loss of gene function. A 2015 study by Rossi et al. (discussed previously) alerted researchers that CRISPR/TALEN knock-out experiments may be subject to such effects. Genetic adaption or compensation had been well known to mouse researchers creating knock-out lines.  In fact, one of our company founders also ran into this when trying to confirm…

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Pooling only 4 siRNAs increases off-target effects

Pooling only 4 siRNAs increases off-target effects

In a previous post, we showed how siRNA pools with small numbers of siRNAs can exacerbate off-target effects. Low-complexity pools (with 4 siRNAs per gene) should thus lead to overall stronger off-target effects than single siRNAs. This phenomenon was addressed in a bioinformatics paper a few years back.  The authors created a model to predict gene phenotypes based on the combined on-target and off-target effects of siRNAs. The siRNAs were screened either individually (Ambion and Qiagen), or in pools of four…

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Citations of our Nucleic Acids Research Paper

Citations of our Nucleic Acids Research Paper

Our 2014 Nucleic Acids Research paper provides an excellent overview of the siPOOL technology.  Google Scholar shows that our paper has been cited 64 times. To put this into perspective, the 2012 PLoS One paper on C911 controls by Buehler et al. has 72 citations.  C911 controls are probably the most effective way to determine whether a single-siRNA phenotype is due to an off-target effect. These citation numbers show that siPOOLs have good mind share when researchers consider the issue of…

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Performing target validation well

Performing target validation well

Summary This blogpost describes issues encountered in target validation and how to safeguard against poor reproducibility in RNAi experiments. The importance of target validation More than half of all clinical trials fail from a lack of drug efficacy. One of the major reasons for this is inadequate target validation. Target validation involves verifying whether a target (protein/nucleic acid) merits the development of a drug (small molecule/biologic) for therapeutic application. Failing to adequately validate a target can burden a pharma with roughly 800 million to 1.4 billion in…

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Low complexity pooling does not prevent siRNA off-targets

Low complexity pooling does not prevent siRNA off-targets

Summary: Low-complexity siRNA pooling (e.g. Dharmacon siGENOME SMARTpools) does not prevent siRNA off-targets.  It may in fact exacerbate off-target effects.  Only high-complexity pooling (siPOOLs) can reliably ensure on-target phenotypes. Low-complexity pooling increases the number of siRNA off-targets One of the claims often made in favour of low-complexity pooling (e.g Dharmacon siGENOME SMARTpools) is that this pooling reduces the number of seed-based off-target effects compared to single siRNAs. If this were true, we would expect different low-complexity siRNA pools for the same…

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What is the probability of an siRNA off-target phenotype?

What is the probability of an siRNA off-target phenotype?

Summary:   Conventional siRNAs have a high probability of giving off-target phenotypes.  siRNA off-target effects can be reduced by using more specific reagents or narrowing the assay focus (to reduce the number of relevant genes).  Even when the assay is relatively focused, more specific reagents significantly increase the probability of observing on-target effects. Probability of siRNA off-target phenotype depends on reagent specificity and assay biology The probability of getting an off-target effect from an siRNA depends on several factors, the main ones…

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5 factors to consider in multi-gene targeting RNAi screens

5 factors to consider in multi-gene targeting RNAi screens

Summary: Effective functional genomic screening depends on a variety of factors that need to be simultaneously addressed to obtain meaningful results. A recent Cell Reports paper demonstrates this by taking a holistic approach to siRNA screening with the use of multi-isoform/multi-gene targeting to address redundant paralogs and pathways in cancer cells. The case for multi-gene targeting Many RNAi screens use arrayed single gene knockdowns to find genes that play an important role in a biological process. The idea is that a single bullet…

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Is it important to avoid microRNA binding sites during siRNA design?

Is it important to avoid microRNA binding sites during siRNA design?

Summary: To address the question of whether one should avoid microRNA binding sites during siRNA design, we examined whether removing siRNAs that share seeds with native microRNAs would reduce the dominance of seed-based off-target effects in RNAi screening. siRNA design and native microRNA target sites Recently, we discussed a review of genomics screening strategies.  The authors state: RNAi screens are powerful and readily implemented discovery tools but suffer from shortcomings arising from their high levels of false negatives and false positives (OTEs)…

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Disrupting lncRNA function with siPOOLs (RNAi), antisense oligos and CRISPR

Disrupting lncRNA function with siPOOLs (RNAi), antisense oligos and CRISPR

Summary This blogpost covers methods used in the disruption of lncRNA function. Specifically focusing on RNA interference (with siPOOLs), antisense oligos, and CRISPR approaches. Challenges faced with these approaches are addressed. Long non-coding RNAs (lncRNAs) make up a major subgroup of RNAs and are defined as over 200 nucleotides long with limited protein-coding potential. There are three times as many genes producing lncRNAs as opposed to proteins. Numerous studies have described functional roles of lncRNAs in development and disease. This has stimulated major…

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