CRISPRDB is an online database for CRISPR/Cas9
gRNA design. The CRISPR/Cas9 system is widely used for genome editing.
The editing efficiency of CRISPR/Cas9 is mainly determined by the guide RNA (gRNA).
Although many computational algorithms have been developed in recent years, it is still
a challenge to select optimal bioinformatics tools for gRNA design in different experimental settings.
We performed a comprehensive comparison analysis of fifteen public algorithms for gRNA design,
using fifteen experimental gRNA datasets. Based on this analysis, we identified the top-performing algorithms,
with which we further implemented various computational strategies to build ensemble models for performance improvement.
Validation analysis indicates that the new stacking ensemble model had improved performance over any
individual algorithm alone at predicting gRNA efficacy under various experimental conditions.
|What is Stacking?
|The stacking ensemble models has two model layers. The first layer contains
multiple individual models to be assembled, with each model generating prediction
results independently. The second layer is a meta-model that collects the predictions
from the first layer as input features to generate final integrated prediction results.
The stacking method not only can be used to assemble various internal models but also
can be adopted as a framework to integrate various published algorithms.
interpret the gRNA prediction score?
|All the pre-designed gRNAs have prediction scores
between 50 - 100. These scores are assigned by the new
computational gRNA efficiency prediction algorithm. The higher the score, the more
efficient the respective gRNA. That is why the search result is
ordered by prediction score. In our experience, a gRNA
with prediction score > 80 is most likely to be efficient in experiments.
perform gRNA search?
|Search by gene target information. There are three options
to do gRNA search based on target gene: GenBank Accession, NCBI Gene ID or Gene
Symbol. You have to enter the exact ID or symbol and no partial
match is allowed. In this way, a single gene record will be
retrieved and the pre-designed gRNAs that targeting on this gene will be shown.
perform custom prediction?
|CRISPRDB provides additional web interface for more flexible
gRNA design. The users could design gRNAs that targeting on their
own genomic target sequences. To conduct custom prediction, users
should first select a type of promoter system which they would use
the designed gRNAs. CRISPRDB offers gRNA design for the U6 and T7
promoter systems. Next, users should input their genomic target sequence
in the input box. The sequence must be between 31 and 30,000 bases
long, and CRISPRDB only allow one input sequence at a time. Please
note that all spaces, line breaks, and numbers within the submission
will be removed, and multiple lines will be joined together to create
a single sequence. After user-provided sequence input, the prediction algorithm
will search for potential target sites in the input sequence and predict
the efficacy scores of designed gRNAs that targeting on those target sites.
|Who developed CRISPRDB?
|CRISPRDB was created by Xiaowei Wang's lab at
the Department of Pharmacology, University of Illinois at Chicago.