Summary: This blogpost is focussed on ribosomal RNA (rRNA) depletion methods frequently applied to improve and economize RNA-Seq experiments. The Rise of RNA-Seq RNA-Seq¬†Overtakes Microarrays The use of Next-Generation RNA Sequencing (RNA-Seq) has recently overtaken that of DNA-based microarrays to detect and quantify changes in gene expression. Why? RNA-Seq can detect novel coding and non-coding genes, splice isoforms, single nucleotide variants and gene fusions. Its broader dynamic range also enables sensitive detection of low abundance transcripts. Also, technological advancements in Read More
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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 Read More
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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 Read More
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 Read More
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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 Read More
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 Read More
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 Read More
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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 Read More
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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 Read More
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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) Read More
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