CRISPRlnc

Our tool enables more accurate identification of CRISPR targets on non-coding genes for the CRISPRko and CRISPRi mechanisms.

Currently existing sgRNA design tools are developed for coding genes, and our tool CRISPRlnc compensates for the lack of specialized tools for non-coding genes.

To validate the predictive performance of CRISPRlnc, we compared its performance with other tools (attached below is a list of software participating in the performance evaluation) using independent test datasets, and the results showed that our tool could more accurately identify CRISPR targets on non-coding genes in CRISPRko and CRISPRi mechanisms (as shown in the left figures).

Below are the details of our review tools:

Tool Design Guide URL
CRISPRdirect Alignment-Based https://crispr.dbcls.jp/
CHOPCHOP Hypothesis-driven https://chopchop.cbu.uib.no/
CRISPOR Hypothesis-driven http://crispor.tefor.net/crispor.py
CRISPR-GE Hypothesis-driven http://skl.scau.edu.cn/targetdesign/
CCTOP Hypothesis-driven https://cctop.cos.uni-heidelberg.de:8043/
IDT Hypothesis-driven https://www.idtdna.com/site/order/designtool/index/CRISPR_CUSTOM
MIT Hypothesis-driven http://www.genome-engineering.org/
CRISPick Hypothesis-driven https://portals.broadinstitute.org/gppx/crispick/public
SSC Machine Learning http://crispr.dfci.harvard.edu/SSC/
Wu_CRISPR Machine Learning http://crispr.wustl.edu
CRISPRscan Machine Learning https://www.crisprscan.org/sequence/
CRISPRater Machine Learning https://doi.org/10.1093/nar/gkx1268
TUSCAN Machine Learning https://github.com/BauerLab/TUSCAN
CRISPRml(Fusi_Score) Machine Learning https://crispr.ml/#
Wang_score Machine Learning http://crispor.tefor.net/crispor.py
sgRNAScorer(Chari_Score) Machine Learning http://crispr.med.harvard.edu/sgRNAScorer
DeepHF Machine Learning http://www.deephf.com/#/cas9
DeepCas9 Machine Learning http://deepcrispr.info/DeepCas9