CRISPOR - a CRISPR/Cas9 assistant
CRISPOR predicts off-targets in the genome, ranks guides, highlights problematic guides, designs primers and helps with cloning. Try it on http://crispor.org
CRISPOR uses BWA, a few tools from the UCSC Genome Browser (twoBitToFa, bedClip), various R packages and a huge collection of external packages and source code files from published articles, see the file crisporEffScores.py for the exact references or the tool tips when you mouse over the scores on the interactive website or the user's manual http://crispor.org/manual/.
If you need to analyze hundreds of thousands of guides for a library, the tool FlashFry is probably the better tool for you, see https://github.com/aaronmck/FlashFry. That being said, CRISPOR now has .bed input, so as long as you are not running on thousands of exons, it is probably fast enough for most applications.
If you only need efficiency scores and no interactive website, try "python crisporEffScores.py", it is a python module but also has a command line interface that may be sufficient for programmers.
Installation of a CRISPOR website mirror or as a command line tool
In this section, I assume that you are root and you want to setup a local CRISPOR website. If you only want to use the command line tools, the installation commands below would be the same, but 1) you don't need the sudo commands for pip and 2) you can use the option '--user' when running pip to install the tools into your own home directory ~/.local instead of /usr/ and /var/.
If you are unsure what the things below mean or if you just want to try it and not install it or modify your server setup, you may want to try the virtual machine, which is a complete installation of CRISPOR with everything included: http://crispor.org/downloads/
CRISPOR uses python2.7. Change
pip2 in the commands below if your default python is python3.
I do not have plans to change it to python3 right now as it is still pretty easy on most systems
to get a python2 installed. Let me know if this is not the case for your Unix version.
Install BWA and a few required python modules:
# Debian/Ubuntu apt-get install bwa python-pip python-matplotlib sudo pip install biopython numpy==1.14.0 scikit-learn==0.16.1 pandas twobitreader
# Fedora/Centos/Redhat/Scientific Linux yum install bwa python-pip python-devel tkinter sudo pip install biopython numpy==1.14.0 scikit-learn==0.16.1 pandas matplotlib twobitreader
The version of scikit-learn is important. If you want to use a newer version, the Microsoft Azimuth data files have to be upgraded. Contact me or the Microsoft people if you want to do this.
For the Cpf1 scoring model:
sudo pip install keras tensorflow h5py
I'm using the versions 2.1.5, 1.7.0 and 2.7.1 for these, but I hope that the exact version for these is not important.
Install required R libraries for the WangSVM efficiency score:
sudo Rscript -e 'install.packages(c("e1071"), repos="http://cran.rstudio.com/")' sudo Rscript -e 'source("https://bioconductor.org/biocLite.R"); biocLite(c("limma"));'
The R packages have not changed in many years. The version should really not matter at all. In principle, you can remove the wang score from crispor.py in the global variable where the scores are defined and not worry about R anymore. I don't think that as of 2020 anyone is still using this score for designing their guides.
When you run crispor.py, it should then show the usage message:
Usage: crispor.py [options] org fastaInFile guideOutFile Command line interface for the Crispor tool. org = genome identifier, like hg19 or ensHumSap fastaInFile = Fasta file guideOutFile = tab-sep file, one row per guide Use "noGenome" if you only want efficiency scoring (a LOT faster). This option will use BWA only to match the sequence to the genome, extend it and obtain efficiency scores. If many guides have to be scored in batch: Add GGG to them to make them valid guides, separate these sequences by at least one "N" character and supply as a single fasta sequence, a few dozen to ~100 per file. Options: -h, --help show this help message and exit -d, --debug show debug messages, do not delete temp directory -t, --test run internal tests -p PAM, --pam=PAM PAM-motif to use, default NGG. TTTN triggers special Cpf1 behavior: no scores anymore + the PAM is assumed to be 5' of the guide. Common PAMs are: NGG,TTTN,NGA,NGCG,NNAGAA,NGGNG,NNGRRT,NNNNGMTT,NNNNACA -o OFFTARGETFNAME, --offtargets=OFFTARGETFNAME write offtarget info to this filename -m MAXOCC, --maxOcc=MAXOCC MAXOCC parameter, guides with more matches are excluded --mm=MISMATCHES maximum number of mismatches, default 4 --bowtie new: use bowtie as the aligner. Do not use. Bowtie misses many off-targets. --skipAlign do not align the input sequence. The on-target will be a random match with 0 mismatches. --noEffScores do not calculate the efficiency scores --minAltPamScore=MINALTPAMSCORE minimum MIT off-target score for alternative PAMs, default 1.0 --worker Run as worker process: watches job queue and runs jobs --clear clear the worker job table and exit -g GENOMEDIR, --genomeDir=GENOMEDIR directory with genomes, default ./genomes
Testing the script
To test the program, first make sure that there is a directory "../genomes". If it's not there, rename "genomes.sample" to "genomes":
mv ../genomes.sample ../genomes
Then run this command:
mkdir -p sampleFiles/mine/ crispor.py sacCer3 sampleFiles/in/sample.sacCer3.fa sampleFiles/mine/sample.sacCer3.tsv -o sampleFiles/mine/sample.sacCer3.mine.offs.tsv
The files in sampleFiles/mine should be identical to the files in sampleFiles/out/
The file testInHg19.fa contains a sample for the hg19 genome, the output is in testOutHg19.tab and testOutHg19Offtargets.tab
../crispor.py hg19 testInHg19.fa testOutHg19.mine.tab -o testOutHg19Offtargets.mine.tab
To add more genomes than yeast, skip the next section. If you want to run your script now as a web service, continue reading with the next section.
Running the script as a CGI under Apache with the job queue
Make sure you can execute CGI scripts somewhere. Your Apache config (e.g. /etc/apache2/sites-enabled/000-default) should contain a section like this:
<Directory "/var/www/html"> AllowOverride All Options +ExecCGI (...) AddHandler cgi-script .cgi .pl .py
Also make sure you have the CGI module enabled:
sudo a2enmod cgi sudo service apache2 restart
If using SElinux, especially on Fedora/CentOS/RedHat, please switch it off or set it to permissive mode.
Clone the repo into such a directory:
cd /var/www/html/ git clone https://github.com/maximilianh/crisporWebsite
Use the sample E. coli genome for a start:
mv genomes.sample genomes
Create a temp directory with the right permissions:
mkdir temp chmod a+rw temp
Make sure that Apache is allowed to execute the crispor.py script, it should have x and r permissions for all:
ls -la crispor.py # if not ... chmod a+rx crispor.py
By default, the jobs database is a SQlite file, /tmp/crisporJobs.db. The Apache user has to be able to write to it so let us create it now:
./crispor.py --clear Worker queue now empty
Now start a single worker job. It will watch the job queue and process jobs:
Check that your worker is indeed running:
cat log/worker1.log ps aux | grep crispor
Now try to access the script from a webbrowser, http://localhost/crispor.py and click "Submit"
Adding a genome
If you want to add to your own crispor.py installation a genome that is already on crispor.org, that's very easy. All genomes available on crispor.org (except a few pre-publication ones) are provided as pre-indexed and correctly formattef files for download at http://crispor.tefor.net/genomes/. To get one of these into the current directory, use a command like this (replace hg38 with your genome code):
mkdir genomes cd genomes mkdir hg38 cd hg38 wget -r -l1 --no-parent -nd --reject 'index*' --reject 'robots*' http://crispor.tefor.net/genomes/hg38/
If you need to add a new genomes, this is quite a bit more involved. Ideally you want gene models in the right format (GFF), a fastsa file and various tools to convert and index these. In most cases, it's much easier to email firstname.lastname@example.org and ask me to add the genome, then you can download it as above. If this is not what you want, you can add a genome yourself, there even is a script for it. Look into the "tools" directory https://github.com/maximilianh/crisporWebsite/tree/master/tools, try the script crisprAddGenome. You will need to download the UCSC tools
bedToBigBed from http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/ and install the tool
gffread by installing cufflinks on your machine (e.g. with
apt-get install cufflinks).
The subdirectory usrLocalBin contains other required tools for this script, you can copy them into /usr/local/bin of your machine, they are 64bit static linux binaries and should work on most current machines.
The script can auto-download genomes from Ensembl, UCSC or NCBI or allows you to add your own custom genome in .fasta format and .gff.
E.g. to add the X. laevis genome: sudo crisprAddGenome fasta /tmp2/LAEVIS_7.1.repeatMasked.fa --desc 'xenBaseLaevis71|Xenopus laevis|X. laevis|Xenbase V7.1' --gff geneModels.gff3
The four |-split values for the --desc option are: internalDatabaseName, scientificName, commonOrDisplayName, VersionNameOfAssembly
Make sure that internalDatabaseName does not include special characters, spaces etc. as it is used as a directory name.
"I am running many thousands of guides and it is very slow"
The .bed input is always fastest, as it saves the initial BWASW step where crispor maps to the target genome.
If you are using the FASTA input, instead of feeding it a multi-fasta file (where crispor will map every piece to the genome first), try to feed it a single sequence and separate every 23bp-target in it with NN. This means that you will not get the efficiency scores but you can run these separately or in parallel with crisporEfficiencyScores.py.
For a major speedup in processing time, try to put the genome onto the ramdisk:
twoBitToFa genomes/hg19/hg19.2bit /dev/shm/hg19.fa
crispor.py will find the genome file and use bedtools to get the flanking sequences. This is almost 10x faster than the twoBitToFa command (at the cost of more RAM).
Alternatively, you may want to give flashfry by Aaron McKenna a try. It is optimized for large libraries, it uses much more RAM and has fewer scores but is sufficient for most large-library-design applications.
- Jean-Paul Concordet for numerous ideas on the user interface
- Alberto Stolfi for finding the N-SNP-bug
- Mark Diekhans for patching twoBitToFa and making it 100 times faster
- See the file changes.html for the full list of acknowledgements for every feature
- BWA is under GPL3
- libSVM: under copyright by Chih-Chung Chang and Chih-Jen Lin see http://www.csie.ntu.edu.tw/~cjlin/libsvm/COPYRIGHT
- svmlight: free for non-commercial use, see http://svmlight.joachims.org/
- SSC: no license specified
- primer3: GPL2.
- Fusi/Doench score: see LICENSE.txt, (c) by Microsoft Research
- the two files crispor.py and crisporEffScores.py are released under a special license, see LICENSE.txt in this directory