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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 any single siRNA, or even synthetic off-target effects from pooled siRNAs.

Rather than deconvoluting the pool, a better approach is to test with independent reagents.  Should the phenotype be due to the seed effects of an siRNA in the siGENOME pool, the new designs (with presumably different seed sequences) should not show them.  (Note that because they have their own potential complicating off-targets, an even better option would be to use a reagent like siPOOLs that minimises the likelihood of off-target effects).

Independent validation reagents was the approach used by Li et al. in a screen looking for enhancers of antiviral protein ZAP activity.

They first did a genome-wide (18,200 genes) screen with siGENOME pools, looking for pools that increased viral infection rate.

The biggest effect was with the positive control, ZAP (aka ZC3HAV1).   Several other pools also stood out as giving large increases in viral infection (Fig 1B):

They identified 90 non-control genes with reproducible Z-scores above 3 in their replicate experiments (~0.5% of screened genes).

These 90 genes were then tested with 3 Ambion Silencer siRNAs.  (They also included a few genes in the validation round based on pathway information and off-target analysis– more on this below.)

Of the 90 candidate hit genes, only 11 could be confirmed (Fig 2B, note that ZC3HAV1/ZAP is the positive control and JAK1 was added to the validation round based on pathway info.  A gene was considered confirmed if 2 of 3 siRNAs had a Z-score > 3.):


We also see that only 1 of the 7 top hits from the first round (blue genes in the first figure) was confirmed.  This is a common observation in RNAi screens: the strongest phenotypes are mostly due to off-target effects.

Off-target effects are difficult to interpret, even using advanced analysis programs like Haystack or GESS.  The authors tested 4 genes identified by Haystack as targets for seed-based off-targeting.  None of those genes could be confirmed in the validation round.

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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 (Dharmacon siGENOME), in 3 different batcterial-infection assays (B. abortus, B. henselae, and S. typhimurium).

The model assumed that each siRNA silenced its on-target gene to the same level.  For off-target silencing, they used the predictions from TargetScan, a program for calculating seed-based knockdown by miRNAs or siRNAs.

In order to assess model quality, they checked how similar the gene phenotype predictions were when using different reagents types in the same pathogen-infection screen.

The following figure shows the rank-biased overlap (a measure of how similar lists are with regards to top- and bottom-ranked items), when estimating siGENOME off-target knockdown in one of 2 ways:

A) using the maximum TargetScan score for any of the 4 siRNAs in the siGENOME pool

B) using the mean TargetScan score for the 4 siRNAs

If low-complexity pooling increases the degree of off-target effects, we would expect the maximum TargetScan score to produce better model concordance.

And that is what the authors found.  (the two plots show the rank-biased overlap for the top and bottom of the phenotype ranked lists, respectively)

The off-target effect of a 4-siRNA, low-complexity pool is best described by the strongest off-target effect of any of the individual siRNAs.

As discussed in our NAR paper, pooling a minimum of 15 siRNAs is required to reliably prevent off-target effects.

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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 RNAi off-target effects.

We have noticed, however, that in some cases our NAR paper is cited to justify approaches that we do not endorse.

For example, two recent papers (1, 2) cite our paper as support for the use of Dharmacon ON-TARGETplus 4-siRNA pools to reduce the potential for off-target effects.

Our paper shows, however, that high-complexity siRNA pools (> 15 siRNAs) are needed to reliably reduce off-target effects.

We have also discussed how low-complexity siRNA pools can in fact exacerbate off-target effects.

There’s an old saying that any publicity is good publicity, and we are certainly thankful that these authors have referenced our paper, even if we don’t agree with the interpretations.

And we are especially grateful to all the researchers who have purchased siPOOLs and referred to our products in their publications.

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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 gene to give similar phenotypes.  But this is not the case.

Published expression data shows that low-complexity pooling actually increases the number of off-targets.

Kittler et al. (2007) looked at the effect of combining differing number of siRNAs in low to medium complexity siRNA pools (siRNA pools sizes were: 1, 3, 5, 9, and 12).

Their work showed that the number of down-regulated genes (50% or greater silencing) actually increases when small numbers of siRNAs are combined.  Only when larger numbers of siRNAs are combined does the number of off-targets start to drop:



[The figure is based on data from GEO dataset GSE6807.  Down-regulated genes are those whose expression is reduced by 50% or more.  Note that the orange point is taken from our 2014 NAR paper, as we are not aware of other published expression datasets with this many pooled siRNAs.  A few caveats with combining these datasets are that they use different target genes, siRNA concentrations, and the data comes from a different expression platform.]

Low-complexity pooling: a bad solution for siRNA off-targets

Low-complexity pooling does not get rid of the main problem associated with single siRNAs: seed-based off-target effects.   Based the above analysis, it can make it even worse.  It also prevents use of the most effective computational measures against seed effects.

Redundant siRNA Activity (RSA) is a common on-target hit analysis method for single-siRNA screens.  It checks how over-represented the siRNAs for a gene are at the top of a ranked screening list.  If a gene has 2 or more siRNAs near the top of the list, it will score better than a gene that only has a single siRNA near the top of the list.  This is one way to reduce the influence of strong off-target siRNAs.

Correcting single siRNA values by seed medians has also been shown to be an effective way to increase the on-target signal in screens.  This correction is not effective for low-complexity pools, since each pool can contain 3-4 different seeds.

Off-target based hit detection algorithms (e.g. Haystack and GESS) are also only effective for single-siRNA screens.  The advantage of these algorithms is that it permits the detection of hit genes that were not screened with on-target siRNAs.  These algorithms are not effective for low-complexity pool screens.

Our recommendation: do not convert single siRNAs into low-complexity pools, rather use high-complexity siPOOLs to confirm hits

We do not recommend that screeners combine their single siRNA libraries into low-complexity pools (e.g. combining 3 Silencer Select siRNAs for the same target gene).  If possible, it is better to screen the siRNAs individually and then apply seed-based correction, RSA and seed-based hit-detection algorithms.

The time saved by only screening one well per target may prove illusory when the deconvolution experiments show that the individual siRNAs have divergent phenotypes.

It is probably better to deal with off-target effects up front (by screening single siRNAs) than to be surprised by them later in the screen (during pool deconvolution).

Reliable high-complexity siPOOLs, as independent on-target reagents, can then be used to confirm screening hits.

siTOOLs also now has RNAi screening libraries available.  Please contact us for more information.

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Correcting seed-based off-target effects in RNAi screens

Correcting seed-based off-target effects in RNAi screens

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 could correct for the seed effect, the correlation between siRNAs targeting the same gene may improve.

One straightforward way to do seed correction is to subtract the ‘seed median’ from each siRNA.  (The seed median is the median for all siRNAs having the given seed.)

This was the approach used by Grohar et al. in a recent genome-wide survey of EWS-FLI1 splicing (involved in Ewing sarcoma).  They used the Silencer Select library, which has 3 siRNAs per target gene.

After seed correction, there is only minor improvement in the correlation between siRNAs targeting the same gene.  The intra-class correlation (ICC) improves from 0.031 to 0.037.  The ICC for siRNAs with the same 7-mer seed decreases from 0.576 to 0.261.

Although we have reduced the seed-based signal, it has not resulted in a correspondingly large improvement in the gene-based signal.

More sophisticated seed correction can improve reagent correlation

Grohar et al. used a simple seed-median subtraction method to correct their screening results.

A more sophisticated method (scsR) was developed by Franceshini et al. for seed-based correction of screening data.  It corrects using the mean value for siRNAs with the same seed, and weighs the correction using the standard deviation the values.  This allows seeds with a more consistent effect to contribute more to the data normalisation.

Applying the scsR method to the Grohar data, ICC for siRNAs targeting the same gene increases from 0.031 to 0.041.  It is better than the increase with seed-median subtraction (0.037), but is still only a fairly minor improvement (plot created using random selection of 10,000 pairs of siRNAs that target the same gene):


Off-target correction increases double-hit rate in top siRNAs of RNAi screen

The following plot shows the count for single-hit and double-hit genes as we go through the top 1000 siRNAs (of ~60K screened in total).  Double-hit means that the gene is covered by 2 (or more) hit siRNAs.

Despite the small improvement in reagent correlation, the double-hit rate is essentially the same using simple seed-median subtraction or the more advanced scsR method.

Furthermore, the number of double-hits is higher than what we’d expect by chance.

This shows that, despite the noise from off-target effects, there is some on-target signal that can be detected.

siRNAs with the strongest phenotypes remain difficult to interpret

Despite the fact that the double-hit count is higher than expected by change, most of the genes targeted by the strongest siRNAs are single-hits.  siRNAs with the strongest phenotypes remain difficult to interpret.

Seed correction is best suited for single-siRNA libraries.  Low-complexity pools, like siGENOME or ON-TARGETplus, are less amenable to effective seed correction since there are (usually) 4 different seeds per pool.  This reduces the effectiveness of seed-based correction, even though seed-based off-target effects remain the primary determinant of observed phenotypes (as discussed here, here , and here).

The best way to correct for seed-based off-targets is to avoid them in the first place.  Using more specific reagents, like high-complexity siPOOLs, is the key to generating interpretable RNAi screening results.

For help with seed correction or other RNAi screening data analysis with the Phenovault, contact us at

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