CRISPR Target Assessment
Prof. Itay Mayrose, TAU - Plant Evolution, bioinformatics & comparative genomics


CRISTA is based on learning a regression model using the Random Forest algorithm within the machine learning paradigm. CRISTA can be used to determine the propensity of a genomic site to be cleaved by a given sgRNA. CRISTA was trained on a large dataset assembled from published data of genome-wide unbiased methods for CRISPR-Cas9 cleavage sites profiling [1–5]. It accounts for the possibility of bulges and incorporates a wide range of features encompassing those that are specific to the genomic content, features that define the thermodynamics of the sgRNA, and features concerning the pairwise similarity between the sgRNA and the genomic target. Altogether, these form a complex model that can be used to predict the cleavage propensity of a selected genomic site. In contrast to other tools for scoring cleavage scores [6–14], the prediction function used by CRISTA cannot be translated to a simple function composed of the mismatched positions and the number/location of bulges.

If you use CRISTA, please cite:
Abadi S, Yan WX, Amar D, Mayrose I (2017) A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action. PLoS Comput Biol 13(10): e1005807.

The website offers you several functionalities:

  • Predict cleavage scores for a given set of sgRNA::DNA pair

  • This straight-forward use of CRISTA provides the predicted cleavage score for a given pair of a nucleotide target and the corresponding sgRNA. The genomic target can be specified by its genomic sequence (with additional 3 bases upstream from the 5’-end and 3 bases downstream of the PAM site) or the genomic coordinates in the desired genome reference. Due to the availability of a wide range of genomic features for the hg19 dataset, the selection of a cell-line would utilize a broader model of CRISTA. A list of pairs can be uploaded using a csv file in both modes.

  • Detect possible off-targets throughout the genome

  • Given an sgRNA and a genomic assembly, potential targets are detected using the Burrows-Wheeler Aligner (BWA) with the following parameters: “-N -l 20 -i 0 -n 5 -o 3 -d 3 -k 4 -M 0 -O 1 -E 0”. This identifies all targets with up to four mismatches and/or gaps in the 20-nt matching region. For each potential target the cleavage scores are predicted based on the whole collection of features. The targets are then presented in a ranked order according to their CRISTA score. Please note that the user cannot expect an immediate result since, as opposed to most currently available alternatives, CRISTA considers possible bulges within the DNA site or sgRNA.

  • Rank possible targets in a given nuclear sequence

  • Given a nucleotide sequence, all the potential targets within it, i.e., those that are followed by an ‘NGG’ sequence (either in the forward or reverse strand), are detected and ranked according to the cleavage score predicted by CRISTA. Note that 3 bases from the beginning and end of the sequence are disregarded since some of the features implemented in CRISTA require 3 bases from each end.

The score predicted by CRISTA represents the frequency of genomic indels at a given site relative to a cleavage at the on-target of a highly efficient sgRNA. Further details will be made available soon as our manuscript is published.


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