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Low complexity pooling does not prevent siRNA off-targets

Tue, 17 Apr 2018

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?

Fri, 13 Apr 2018

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

Thu, 05 Apr 2018

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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The post 5 factors to consider in multi-gene targeting RNAi screens appeared first on siTOOLs Biotech Blog.



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

Tue, 13 Mar 2018

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

Wed, 28 Feb 2018

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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The post Disrupting lncRNA function with siPOOLs (RNAi), antisense oligos and CRISPR appeared first on siTOOLs Biotech Blog.



Correcting seed-based off-target effects in RNAi screens

Tue, 20 Feb 2018

Summary: Correcting for seed-based off-targets can improve the results from RNAi screening.  However, the correlation between siRNAs for the same gene is still poor and the strongest screening hits remain difficult to interpret. Seed-based off-target correction has little effect on reagent reproducibility Given that seed-based off-targets are the main cause of phenotypes in RNAi screening, trying to correct for those effects makes good sense. The dominance of seed-based off-targets means that independent siRNAs for the same gene usually show poor correlation. If one...

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The post Correcting seed-based off-target effects in RNAi screens appeared first on siTOOLs Biotech Blog.



Novel anti-cancer mechanism identified by shRNA/siRNA off-target effects

Tue, 12 Dec 2017

RNAi off-target effects takes an interesting turn for cancer research as reported in eLIFE by Putzbach et. al. A specific group of survival genes in cancer cells was revealed thanks to the off-target effects of siRNAs/shRNAs.

The post Novel anti-cancer mechanism identified by shRNA/siRNA off-target effects appeared first on siTOOLs Biotech Blog.



Little correlation between Dharmacon siGENOME and ON-TARGETplus reagents

Tue, 07 Nov 2017

The most common way to validate hits from Dharmacon siGENOME screens is to test the individual siRNAs from candidate pool hits (siGENOME reagents are low-complexity pools of 4 siRNAs). ¬†In this deconvolution round, we normally see that the individual siRNAs for genes behave very differently and seed effects dominate (discussed here and here). One could argue that deconvolution is not the correct way to validate candidate hits (even though it’s the method recommended by Dharmacon), ¬†as testing the siRNAs individually...

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Orthogonal design in software and RNAi screening

Tue, 19 Sep 2017

The software engineering classic The Pragmatic Progammer popularised the benefits of orthogonality in software design. ¬†They introduce the concept by describing a decidedly non-orthogonal system: You’re on a helicopter tour of the Grand Canyon when the pilot, who made the obvious mistake of eating fish for lunch , suddenly groans and faints. Fortunately, he left you hovering 100 feet above the ground. You rationalize that the collective pitch lever [2] controls overall lift, so lowering it slightly will start a...

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Understanding Gene Networks with Combinatorial Gene Knockdown

Thu, 24 Aug 2017

Genes hardly ever work alone, functioning instead in complex gene networks. ¬†Increasing advances in genomics and proteomics and corresponding developments in computational analysis,¬†has really put this into perspective. A recent large scale RNAi study by Novartis found this hairball of a gene network in cancer cells: A gene “hairball” As such, the standard approach of disrupting the expression of a single gene to study loss-of-function phenotypes may not accurately reveal its genetic function. The highly redundant nature of signalling pathways...

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The post Understanding Gene Networks with Combinatorial Gene Knockdown appeared first on siTOOLs Biotech Blog.







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