CRISPRme: variant-aware CRISPR off-target nomination

Run CRISPRme on your own machine

CRISPRme is open source and actively maintained, and you can run the web interface locally on your own machine with the same functionality as before. The previous hosted application at crisprme.di.univr.it is no longer available, because institutional hosting at the University of Verona ended for this service. All features remain available to you.

You have two easy paths to run CRISPRme on your own machine, both with the same functionality as the previous hosted version:

Use the command line version for scripted or batch analyses.
Deploy the web interface locally for the same point-and-click experience as the hosted app.

View on GitHub Installation instructions →

Step-by-step: install and launch the web interface

Follow the three steps below to go from a clean machine to a running local web interface — equivalent to the retired hosted version. Pick the track that matches how you want to install CRISPRme: Conda / Mamba or Docker. Both tracks cover the same three steps:

Install CRISPRme (and activate the environment).
Set up the legacy reference database (one-time download).
Launch the local web interface and open it in your browser.

The one-time setup in Step 2 automatically downloads and configures:

hg38 reference genome
1000 Genomes Project Phase 3 dataset
HGDP variant dataset
Sample metadata files
Default annotations and PAM definitions

Step 1 — Install CRISPRme and activate the environment

Configure the Bioconda channels once, then create and activate the crisprme environment.

# Configure Bioconda channels (one-time)
mamba config --add channels bioconda
mamba config --add channels defaults
mamba config --add channels conda-forge
mamba config --set channel_priority strict

# Create and activate the environment
mamba create -n crisprme python=3.8 crisprme -y
mamba activate crisprme

# Verify the installation
crisprme.py --version

If you use conda rather than mamba, replace mamba with conda in every command.

Step 2 — Set up the legacy reference database (one-time)

# Choose a working directory
CRISPRME_DIR="$HOME/my_crisprme_run"

# Download and configure all reference data (~410 GB)
crisprme.py setup --path "$CRISPRME_DIR"

Optional: to test the installation before downloading the complete dataset, perform a chromosome-specific setup first:

crisprme.py setup --chrom chr22 --path "$CRISPRME_DIR"

The setup process is resumable. If a download is interrupted, simply rerun the same command and CRISPRme will continue from the last completed step.

Step 3 — Launch the local web interface

mamba activate crisprme
cd "$CRISPRME_DIR"           # the directory you chose in Step 2
crisprme.py web-interface    # starts the local server on port 8080

Step 1 — Install Docker and pull the CRISPRme image

Install Docker for your operating system using the official guides for macOS, Windows, or Linux. Then verify the installation and pull the CRISPRme image:

# Verify Docker is working
docker run hello-world

# Pull the latest pre-built CRISPRme image
docker pull pinellolab/crisprme

Step 2 — Set up the legacy reference database (one-time)

Create and enter a working directory on your host machine, then run the setup command inside the container. The directory is mounted into the container as /DATA, so all data is written to your host.

# Choose and enter a working directory
mkdir -p "$HOME/my_crisprme_run"
cd "$HOME/my_crisprme_run"

# Download and configure all reference data (~410 GB)
docker run -v ${PWD}:/DATA -w /DATA -i pinellolab/crisprme \
  crisprme.py setup --path /DATA

Optional: to test the installation before downloading the complete dataset, perform a chromosome-specific setup first:

docker run -v ${PWD}:/DATA -w /DATA -i pinellolab/crisprme \
  crisprme.py setup --chrom chr22 --path /DATA

The setup process is resumable. If a download is interrupted, simply rerun the same command and CRISPRme will continue from the last completed step.

Step 3 — Launch the local web interface

Run the web interface from the same working directory. The -p 8080:8080 flag forwards the container port to your host so the browser can reach the server.

cd "$HOME/my_crisprme_run"   # the directory you set up in Step 2
docker run -v ${PWD}:/DATA -w /DATA -p 8080:8080 \
  pinellolab/crisprme crisprme.py web-interface

Open the interface in your browser

After the server starts, open one of the supported browsers (Chrome, Firefox, or Safari) and navigate to:

http://127.0.0.1:8080

If CRISPRme is running on a remote server or HPC node, replace 127.0.0.1 with the server's IP address.

Important: the web interface must be started from a valid CRISPRme working directory containing the expected folders (Genomes/, VCFs/, PAMs/, Annotations/, etc.), otherwise datasets and configuration files will not be detected. If you are unsure about the expected project structure, refer to the directory structure documentation.

Advanced Resources & Documentation

The quick-start guide above is sufficient to get the local web interface running. For detailed usage instructions, advanced configuration options, and complete workflow documentation, consult the guides below.

Web Interface User Guide

Learn how to configure analyses, monitor jobs, explore results, generate personal risk cards, and use all visualization and reporting features available through the graphical interface.

Open Detailed User Guide

CLI Setup & Usage Guide

Complete documentation for installation, dataset management, command-line workflows, custom VCF integration, PAM definition, automation, and large-scale analyses.

Open CLI Guide

Documentation

Citation

Cancellieri S, Zeng J, Lin LY, Tognon M, Nguyen MA, Lin J, Bombieri N, Maitland SA, Ciuculescu MF, Katta V, Tsai SQ, Armant M, Wolfe SA, Giugno R, Bauer DE, Pinello L. Human genetic diversity alters off-target outcomes of therapeutic gene editing. Nat Genet. 2023 Jan;55(1):34-43. doi: 10.1038/s41588-022-01257-y.

Contacts

Luca Pinello: lpinello@mgh.harvard.edu
Rosalba Giugno: rosalba.giugno@univr.it
Daniel Bauer: bauer@bloodgroup.tch.harvard.edu