Correcting seed-based off-target effects in RNAi screens

Correcting seed-based off-target effects in RNAi screens

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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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Little correlation between Dharmacon siGENOME and ON-TARGETplus reagents

Little correlation between Dharmacon siGENOME and ON-TARGETplus reagents

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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
Orthogonal design in software and RNAi screening

Orthogonal design in software and RNAi screening

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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

Understanding Gene Networks with Combinatorial Gene Knockdown

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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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“Phenoville” – RNAi & CRISPR Screening Strategies

“Phenoville” – RNAi & CRISPR Screening Strategies

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Pleasantville is a movie based on an interesting idea: two teenagers are magically transported through their TV to a town called Pleasantville set in the 1950s where everything is perfect (and also black-and-white).  As they discover the complex, imperfect emotions hidden below the idyllic surface, the black-and-white characters and objects start to gain colour. In loss-of-function genetic screening, some reagents and screening formats may also give rise to a narrow, black-and-white view of a biological process.  A sort of “Phenoville”.  This was illustrated nicely…

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CRISPR/Cas9 Screening – The “Copy-Number Effect”

CRISPR/Cas9 Screening – The “Copy-Number Effect”

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Several CRISPR/Cas9 screens identifying essential genes in cancer cell lines have been performed to date (Shalem et al., 2014, Hart et al., 2015, Kiessling et al., 2016). These typically take the form of pooled screens where sgRNA libraries targeting all genes or subsets of genes are introduced in parallel into Cas9-expressing cells, at a single sgRNA per cell. The sgRNAs exert a negative or positive selection pressure on cells based on their impact on cell viability and proliferation. The most…

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siRNA vs shRNA – applications and off-targeting

siRNA vs shRNA – applications and off-targeting

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Short interfering RNA (siRNA) and short hairpin RNA (shRNA) are both used in RNAi-mediated gene silencing. In this blogpost, we explore the differences in applications of siRNA and shRNA and compare their capacity for off-targeting. For a summary of their properties, please refer to Table 1 at the end of  the post. In what situations should we use siRNA or shRNA? In terms of application, siRNAs are commonly applied for rapid and transient knockdown of gene expression. It is performed in…

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Unexpected Mutations after CRISPR in vivo editing – post-commentary

Unexpected Mutations after CRISPR in vivo editing – post-commentary

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You might have heard or participated in the global discussion over the recently published Nature Commentary that described >1000 off-target mutations in CRISPR-edited mice. The paper reported a small study involving three mice but gained enough virality online to trigger a significant drop in share prices of companies founded based on CRISPR gene-editing – Editas Medicine, CRISPR Therapeutics and Intellia Therapeutics. Here is a summary of the study, with respective concerns raised by the scientific community regarding the validity of…

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Making sense of siGENOME deconvolution

Making sense of siGENOME deconvolution

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As discussed previously, deconvoluted Dharmacon siGENOME pools often give surprising results.  (Deconvolution is the process of testing the 4 siRNAs in a pool individually.  This is usually done in the validation phase of siRNA screens.) One way to compare the relative contribution of target gene and off-target effects is to calculate the correlation between reagents having the same target gene or the same seed sequence.  One of the first things we do when analysing single siRNA screens is to calculate a…

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