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shERWOOD-UltramiR shRNA

Targeting Human, Mouse and Rat Genomes

shERWOOD-UltramiR Highlights

  • shERWOOD algorithm design for superior knockdown performance
  • UltramiR scaffold for increased shRNA processing and potency
  • Minimized off-target effects
  • All transcripts for a gene are targeted for the most complete knockdown
  • Every shRNA is guaranteed to knockdown its intended target*

Superior knockdown with shERWOOD design

shERWOOD-UltramiR shRNA reagents are next generation vector-based RNAi triggers designed using the proprietary shERWOOD algorithm developed and validated in Dr. Gregory Hannon's laboratory at Cold Spring Harbor Laboratory (Knott et al, 2014). An alternate microRNA scaffold "UltramiR" has been optimized for increased shRNA processing and potency based on new information on the key determinants for primary microRNA processing (Auyeung et al, 2013).
The shERWOOD algorithm is based on the functional testing of over 250,000 shRNA sequences using a high-throughput sensor assay (Knott et al, 2014) and uses key sequence characteristics for predicting shRNA potency to select the rare shRNA designs that are potent at single copy representation in the genome. shERWOOD designs have been applied to the creation of new shERWOOD-UltramiR shRNA collections targeting human, mouse and rat genomes.

Minimized off-target effects

Knockdown specificity of the shERWOOD Ultramir shRNA is highly improved compared to classic stem loop shRNA. This is consistent with publications showing that classic stem loop shRNA can cause significant off-target effects and toxicity (Baek et al, 2014). Several reports have shown that off-target effects can be ameliorated by expressing the same targeting sequence in a primary microRNA scaffold (shRNA-miR).

Molecular Cell (2014)

A Computational Algorithm to Predict shRNA Potency

Simon R.V. Knott, Ashley R. Maceli, Nicolas Erard, Kenneth Chang, Krista Marran, Xin Zhou, Assaf Gordon, Osama El Demerdash, Elvin Wagenblast, Sun Kim, Christof Fellmann and Gregory J. Hannon

*All shRNA constructs in a target gene set are guaranteed to knock down mRNA expression by >70%. Cell line of choice should demonstrate expression of the target gene using the non-targeting controls and should demonstrate gene knockdown using positive control shRNA (targeting PTEN or GAPDH).

Find shERWOOD-UltramiR shRNA

Technical Details


Genomics Resources

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Harnessing the endogenous microRNA pathway to trigger RNAi

Lentiviral, inducible lentiviral and retroviral vector options with choice of promoter

  • Deliver to a wide range of cell types including primary and non-dividing
  • Fluorescent marker allow direct visual detection of shRNA expression
  • Inducible or constitutive promoter options
  • Easily create stable cell lines with efficient integration and antibiotic selection

Mammalian promoters may differ in expression level or be silenced over time depending on the target cell line. Variation in expression level can also affect fluorescent marker expression as well as knockdown efficiency. shERWOOD UltramiR shRNA are offered in a choice of promoters for optimal expression, and can be delivered by transfection or transduction.

The ZIP lentiviral vector is available with many different promoter options (SFFV, human CMV, murine CMV, human EF1a, murine EF1a, TRE-3G) driving expression of shRNA (see schematic on the right). The fluorescent marker and shRNA are on the same transcript allowing the level of fluorescence in the cell to be used as a direct indication of shRNA expression through visual inspection.

The mouse CMV promoter expresses efficiently in a number of human and mouse cell lines and is standard in the ZIP lentiviral vector target gene sets. However, for cell lines where a different promoter may be optimal, the ZIP Promoter Selection Kit is available to quickly test for optimal expression in your target cell line. Simply use the provided pre-packaged viral particles from a panel of vectors expressing ZsGreen from the different promoters to easily detect expression efficiency. 

shERWOOD shRNA are expressed with the optimized ultramiR scaffold. The figure below shows the shRNA secondary structure and highlights the sequences that are included in the mature RNAi trigger bound to the targeted mRNA.

Figure 1. Schematic of shERWOOD-UltramiR shRNA. (A) Passenger (green) and Guide (orange) strand are shown with Dicer and Drosha nuclease cleavage sites are in red. (B) The final step of shRNA processing loads the Guide Strand (orange) into the RISC complex which binds the target mRNA (blue) in a sequence specific manner.   


  • Baek, et al. 2014. Off-target effect of doublecortin family shRNA on neuronal migration associated with endogenous microRNA dysregulation. Neuron 82, 1255–1262.
  • Knott et al., 2014. A Computational Algorithm to Predict shRNA Potency. Mol. Cell. 56(6):796–807
  • Auyeung et al., 2014. Beyond Secondary Structure: Primary-Sequence Determinants License Pri-miRNA Hairpins for Processing. Cell 152(4):844–858
  • Fellmann et al., 2011. Functional identification of optimized RNAi triggers using a massively parallel sensor assay. Mol Cell. 18; 41(6):733-46.
  • Castanotto, 2007. Combinatorial delivery of small interfering RNAs reduces RNAi efficacy by selective incorporation into RISC. Nucleic Acids Res. 35(15):5154-64.
  • Grimm et al., 2006. Fatality in mice due to oversaturation of cellular microRNA/short hairpin RNA pathways. Nature 441, 537-541.
  • McBride et al., 2008. Artificial miRNAs mitigate shRNA-mediated toxicity in the brain: Implications for the therapeutic development of RNAi. PNAS 105; 15, 5868-5873
  • Beer et al., 2010. Low-level shRNA Cytotoxicity can contribute to MYC-induced hepatocellular carcinoma in adult mice. Mol Ther 18(1):161-170.
  • Pan et al., 2011. Disturbance of the microRNA pathway by commonly used lentiviral shRNA libraries limits the application for screening host factors involved in hepatitis C virus infection. FEBS Lett. 6;585(7):1025-1030.
  • Huesken, D., et al. 2005. Design of a genome-wide siRNA library using an artificial neural network. Nat. Biotechnol. 23, 995–1001.
  • Vert, J.P., et al., 2006. An accurate and interpretable model for siRNA efficacy prediction. BMC Bioinformatics 7, 520.
  • Li, L., et al.,2007. Defining the optimal parameters for hairpin-based knockdown constructs. RNA 13, 1765–1774
  • Bassik, M.C., et al.,2009. Rapid creation and quantitative monitoring of high coverage shRNA libraries. Nat. Methods 6, 443–445.
  • Castanotto, D 2011. Sensor and Sensitivity: A Screen for Elite shRNAs. Molecular Therapy 19, 5, 823-825
  • Moffat et al, 2006. A Lentiviral RNAi Library for Human and Mouse Genes Applied to an Arrayed Viral High-Content Screen. Cell, 124, 6, 1283-1298.
  • Silva, et al. 2005. Second-generation shRNA libraries covering the mouse and human genomes. Nat Genet 37, 1281-1288.
  • Boden et al, 2004. Enhanced gene silencing of HIV-1 specific siRNA using microRNA designed hairpins. Nucleic Acids Res 32, 1154-1158.


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