Uncover New Insights into Gene Function, Reagents and Screening Data

Identifying genes responsible for imparting phenotypes is a challenge. In the past years, large RNA interference (RNAi)-based functional genomic screens were carried out to identify novel factors that impact relevant phenotypes. However, due to prevalent RNAi off-target effects and variable siRNA knock-down efficiencies, results are often dominated by false positives or previously known strong-inducing factors.

The Phenovault is a carefully curated database and analysis suite for RNAi/CRISPR screens that combines big data with dominant siRNA seed-based behaviour to help scientists uncover novel gene targets and data insights. With the largest collection of RNAi screening data and extensive data analysis algorithms, the Phenovault serves as a useful target validation and discovery tool for scientists looking to leverage past and current functional genomic screening data.

Largest collection of RNAi screening data with Phenovault

Amassing genome-scale screening data that often lies buried within publication supplementary materials, the Phenovault is the largest curated collection of RNAi screening data linking phenotypic read-outs to reagent sequences or ID. Inside the Phenovault: ≥ 48 publications, 374, 364 unique reagents, 20 million data points and 845 screening features.

Phenovault RNAi Reagents

Screencount per Phenovault RNAi Reagents

Harnessing Dominant siRNA Seed Effects with Phenovault

Numerous publications together with our own observations indicate that seed-based siRNA off-targeting dominates most RNAi screens. The Phenovault is equipped with published and proprietary analysis algorithms that exploit this behaviour to recognise and extract genes that may impact phenotypes but are often missed by on-target analysis.

Seed is by far the strongest phenotypic determinant

What is a seed?
The seed is a 6-base sequence at position 2 to 7 of the siRNA guide strand that dictates siRNA off-target activity based on microRNA mimicry – microRNAs use the seed to recognize and downregulate their targets via deadenylation-induced degradation or translation inhibition.

Seed effects dominate phenotypes
Correlation analysis of phenotypes produced by two siRNAs sharing the same gene target vs same seed sequence demonstrates seed-based off-targeting often dominates RNAi-based phenotypes. The figure shows ~280 million pairwise correlations based on data from a multi-parametric RNAi screen where > 100, 000 siRNAs were screened for > 15, 000 genes.

Phenovault Applications

Discover novel targets

Identify novel genes that impact phenotypes by seed-based analysis of RNAi screening data.

Evaluate gene targets

Validate targets by retrospective analysis of gene disruption phenotypes produced in Phenovault screens.

Counteract screening artefacts and siRNA off-targeting

Uncover screening artefacts (e.g. plate positional effects) and improve hit list with siRNA seed-based correction


Identify relevant gene combinations

Discover gene combinations that impart relevant phenotypes with siTOOLs' Merlin Analysis algorithm.

Evaluate siRNA reagents

Determine potential for siRNA off-targeting and obtain candidate off-target gene list.

Assess quality of hit list

For a given screening dataset, uncover likely false positives and obtain predicted expression levels.

What customers say about Phenovault

"My group worked with siTOOLs to analyze several siRNA screening datasets. Using Phenovault we were able to extensively analyze on- and off-target effects and ultimately identify several interesting cancer targets and pathways. Considering the complexity of the siRNA activity, phenovault provide very valuable information enabling to fully capitalize on experimental data. I would definitively recommend Phenovault to all investigators involved with siRNA screening."

Jean Philippe Stephan
Director Center of Excellence Pharmacological Screening, Compound Management and Biobanking, Servier Research Institute, France 

"Phenovault supported us in our siRNA analysis, by supporting both on the on-target as well as the seed mediated off-target effects. Through in-depth scientific discussions on the data and the analysis, we jointly managed to get the maximum out of our data. This was a very fruitful collaboration which we will surely repeat."

 Beatrice Coornaert
Group Leader in vitro Biology, Galapagos NV, Belgium