CRISPRon: enhanced data-driven on-target CRISPR-Cas9 gRNA efficiency prediction
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 bioconda -c conda-forge -n crispron python=3 tensorflow=2
conda install -y -c bioconda -c conda-forge -n crispron biopython viennarna=2.2.5
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
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
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 software+crispron@rth.dk