The discovery of RNAi promised a new era in which the power of genetics could be applied to model organisms for which large-scale studies of gene function were previously inconvenient or impossible. Yet, it quickly became clear that implementing RNAi technology, especially on a genome-wide scale, could be challenging. This was particularly true for applications in mammalian cells wherein discrete sequences, in the form of siRNAs or shRNAs, were used as silencing triggers. The overall degree of knockdown achieved was found to vary tremendously, depending upon the precise sequence of the small RNA that is loaded into the RNAi effector complex (RISC). Yet, the sequence and structural motifs that favor RISC loading and high turnover target cleavage have, until now, not been been revealed.

The shERWOOD Algorithm

Unlike for shRNAs, several accurate algorithms have been developed for siRNA design. The development of accurate siRNA design algorithms was only made possible with the creation of a large training dataset. Until recently, a corresponding shRNA dataset has been lacking. Recently, however, a “sensor” method has been developed that allows for the parallel assessment of shRNA potencies on a massive scale. Since the development of the sensor algorithm, we have interrogated the potency of ~250,000 shRNAs and we have leveraged this dataset to train an shRNA-specific prediction algorithm which we call shERWOOD. shERWOOD is based on the machine learning method called Random Forrests. The algorithm iteratively bootstraps observations (which in our case are shRNA guide sequences and corresponding sensor measurements). For each bootstrapped sample, a decision tree is built that optimally separates the observations based on their sensor scores, using the most predictive sequence characteristics. The result is a set of decision trees (or a forrest) where each decision tree is designed to optimally separate a subset of the training data. When a new shRNA guide is given to the trained algorithm, the sequence is run through all decision trees and the mean of tree outputs is reported as a score. For further details about the shERWOOD algorithm please see the corresponding manuscript: link

For more information, please read the shERWOOD manuscript.

The shERWOOD-UltramiR shRNA Libraries

We have combined the predictive power of the shERWOOD algorithm with optimized design strategies for limiting off-target effects and maximizing transcript coverage and constructed Human and Mouse genome-wide shRNA libraries. The libraries are constantly evolving and are currently housed within a MSCV based retroviral backbone. The libraries were initially constructed in traditional mir30 micro-RNA scaffolds, however they are currently being transferred MSCV retroviral, lentiviral and doxycycline-inducible vectors harboring an optimized micro-RNA scaffold. In this alternative scaffold, which we term ultramiR, the restriction sites used for traditional cloning of shRNA sequences are removed. These modifications cause a ~2-3 fold increase in shRNA processing, and this results an increase in shRNA knockdown efficiency.

The libraries are distributed through Transomic Technologies.

For more information, please read the shERWOOD manuscript.

Hannon Laboratory

The Hannon lab focuses on three major areas of biology. For the past decade, we have sought to understand the biological roles of small RNAs and the underlying mechanisms by which they operate. We have identified and characterized many of the major biogenesis and effector complexes for small interfering RNAs and microRNAs, including Dicer, RISC, and elements of the Microprocessor. Over the past several years, we have focused on roles of small RNAs in germ cells, which tend to have the most elaborate set of small RNA pathways of any cell type. This led to the discovery of an essential role for pseudogenes in producing small RNAs that are critical for proper oocyte development and to the discovery of an elegant small RNA-based immune system that guards the genome against transposable elements. The latter system incorporates another small RNA class, piRNAs, into an adaptive cycle that both responds to transposon challenge and can communicate epigenetic information about that challenge from parent to progeny. The Hannon lab also strives to understand the biology of cancer cells, with a focus on breast and pancreatic cancer. Here, we are interested in the roles of small RNAs as oncogenes and tumor suppressors and in exploiting the RNAi libraries that we have developed to identify new therapeutic targets for specific disease subtypes. Finally, we are taking genetic approaches to understand the biology of resistance to currently used targeted therapies. The third component of the laboratory exploits the power of next generation sequencing to understand the biology of the mammalian genome. Our efforts range from the identification of new classes of small RNAs to understanding human evolution and diversity. Most recently, we have placed a major emphasis on the evolution of the epigenome and its role in driving cell fate specification.

For more information, please visit the The Cold Spring Harbor Hannon Lab's website.

Cold Spring Harbor Laboratory

Founded in 1890, Cold Spring Harbor Laboratory (CSHL) has shaped contemporary biomedical research and education with programs in cancer, neuroscience, plant biology and quantitative biology. CSHL is ranked number one in the world by Thomson Reuters for the impact of its research in molecular biology and genetics. The Laboratory has been home to eight Nobel Prize winners. Today, CSHL's multidisciplinary scientific community is more than 600 researchers and technicians strong and its Meetings and Courses program hosts more than 12,000 scientists from around the world each year to its Long Island campus and its China center. Tens of thousands more benefit from the research, reviews, and ideas published in journals and books distributed internationally by CSHL Press. The Laboratory's education arm also includes a graduate school and programs for middle and high school students and teachers. CSHL is a private, not-for-profit institution on the north shore of Long Island.

For more information, please visit the CSHL Website.

License Information

For large scale projects, please contact us. For licensing information for non-academic institutions and for all comercial purposes, please contact Vladimir Drozdoff at the Office of Technology Transfer.