CRISPRon: enhanced data-driven on-target CRISPR-Cas9 gRNA efficiency prediction
If you are just looking for ways to design or analyse your CRISPR sgRNAs, you may find our webserver at https://rth.dk/resources/crispr/crispron/ an easier starting point.
The software has been tested with the following versions, but later versions of CRISPRoff, python, biopthon, and tensorflow should also work.
- biopython : 1.78
- python : 3.9.2
- tensorflow : 2.4.1
- viennarna : 2.2.5
- CRISPRoff : 1.1.2
You need to install CRISPRoff (version 1.1.2 or later) which can be downloaded from
https://rth.dk/resources/crispr/crisproff/download
After downloading and un-packing, you should move CRISPRspec_CRISPRoff_pipeline.py to the bin folder of CRISPRon and energy_dics.pkl to data/model/energy_dics.pkl
The easiest way to get the needed prerequisites to run CRISPRon is through conda. If you have conda installed already you can skip this step, otherwise go to https://docs.conda.io/en/latest/miniconda.html to learn how to install conda on your system. Once conda is correctly installed. You need to install the crispron requirements with
conda create -y -c biocanda -c conda-forge --name crispron --file environment.yml
Later versions are also expected to work. However, the program depends on RNAfold and versions other than 2.2.5 of the ViennaRNA package will give slightly different results.
Assuming you have installed the prerequisites in a conda environment called crispron, you can run the built-in software test
conda activate crispron
./bin/test.sh
Which should end with
TEST ok
Note that the test requires diff from diffutils which must be installed as a normal package on your system.
Assuming you have installed the prerequisites in a conda environment called crispron, you can run the software on the test data
conda activate crispron
./bin/CRISPRon.sh test/seq.fa test/outdir
Output files
The script CRISPRon.sh outputs the following files:
- 23mers.fa: the 23 nt target + PAM sequences extracted from the input FASTA file
- 30mers.fa: the 30 nt prefix + target + PAM + suffix sequences extracted from the input FASTA file
- CRISPRparams.tsv: a tab-separated table containing the free energy changes computed by the CRISPRoff software. These are the RNA-DNA hybridisation energy (both unweighted and weighted), the DNA-DNA opening energy, the RNA-RNA spacer self-folding energy, and the CRISPRoff score. For details, read about CRISPRoff at https://rth.dk/resources/crispr/
- crispron.csv: a comma-separated table containing the 30 nt prefix + target + PAM + suffix sequences and the CRISPRon predicted indel frequencies
To run the software on your own data, first construct a FASTA file with all the sequences you want to have tested. See test/seq.fa for FASTA format. Just remember that the program needs at least 30 nucleotides to fit the full target
prefix (4nt) -- target (20nt) -- PAM (3nt, NGG) -- suffix (3nt)
And then run the program with your own fasta file and an appropriate output directory.
Running on the sequence the
>test
ACTGAACTTGAAAAGCAAAAAGAAACTGGCCATACTTTCGAAGAAATGCTACTGACTG
You will get the following output in crispron.csv
| ID | 30mer | CRISPRon |
|---|---|---|
| test_p_7 | TGAACTTGAAAAGCAAAAAGAAACTGGCCA |
11.48 |
| test_m_30 | GTCAGTAGCATTTCTTCGAAAGTATGGCCA |
37.06 |
Which means that CRISPRon finds two possible 30mers with targets + PAM sequence inside. In the first, test_p_7, the target starts on position 7 on the plus strand. In the second, test_m_30, the target is on the negative strand and the PAM + target is found on position 30 in the test sequence as the reverse complement to
CCATACTTTCGAAGAAATGCTAC
which is
GTAGCATTTCTTCGAAAGTATGG
The data used for training CRISPRon v. 1.0 may be downloaded from the official CRISPRon page at (https://rth.dk/resources/crispr/crispron/download)
The DeepCRISPRon_train.py is run like this
export OPT=adam
export LEARN=0.0001
export EPOCHS=3000
export N_VAL=6
export N_MOD=5
export SEQ_C=30mer_gRNA
export VAL_C=Quant_norm_efficiency
export VAL_G=CRISPRoff
export BATCH_SIZE=500
export PREF="validation_set"
export DT= DeepCRISPRon_train.py
export SEED=0
export TYPE=CG
export M=2
python3 $DT $OPT $LEARN $EPOCHS $SEQ_C $VAL_C $VAL_G $N_VAL $M $BATCH_SIZE $SEED $TYPE $PREF*
With the options above, you would need a partition of the data in 6 datasets, where the 6th will be held out. The data sets should be named validation_set1.csv .. ..6.csv, and the sixth will be held out. With M=2, the second data set would be used for validation (early stopping).
How to partition the data is described in the paper
Xiang, X., Corsi, G.I., Anthon, C. et al. Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning. Nat Commun 12, 3238 (2021). https://doi.org/10.1038/s41467-021-23576-0
Copyright 2021 by the contributors (see AUTHORS file)
This is a free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this software, see LICENSE. If not, see http://www.gnu.org/licenses/.
If you use CRISPRon in your publication please cite
CRISPRon: enhanced data-driven on-target CRISPR-Cas9 gRNA efficiency prediction. Xiang X, Corsi GI, Anthon C, Qu K, , Pan X, Liang X, Han P, Dong Z, Liu L, Zhong J, Ma T, Wang J, Zhang X, Jiang H, Xu F, Liu X, Xu X, Wang J, Yang H, Bolund L, Church GM, Lin L, Gorodkin J, Luo Y. Submitted.
The data used for training of CRISPRon comes in part from the paper from Kim 2019, please cite that as well
SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance. Kim, H.K. et al. Sci Adv 5, eaax9249 (2019).
If you use CRISPRspec / CRISPRoff in your publication please cite
CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters. Alkan F, Wenzel A, Anthon C, Havgaard JH, Gorodkin J Genome Biol. 2018 Oct 26;19(1):177
In case of problems or bug reports, please contact [email protected]