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install with bioconda GitHub release (latest by date) Conda license

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CRISPRme

CRISPRme is a comprehensive tool designed for thorough off-target assessment in CRISPR-Cas systems. Available as a web application (http://crisprme.di.univr.it/), offline tool, and command-line interface, it integrates human genetic variant datasets with orthogonal genomic annotations to predict and prioritize potential off-target sites at scale. CRISPRme accounts for single-nucleotide variants (SNVs) and indels, considers bona fide haplotypes, and allows for spacer:protospacer mismatches and bulges, making it well-suited for both population-wide and personal genome analyses. CRISPRme automates the entire workflow, from data download to executing the search, and delivers detailed reports complete with tables and figures through an interactive web-based interface.

Table Of Contents

0 System Requirements
1 Installation
  1.1 Install CRISPRme via Conda/Mamba
    1.1.1 Installing Conda or Mamba
    1.1.2 Installing CRISPRme
    1.1.3 Updating CRISPRme
  1.2 Install CRISPRme via Docker
    1.2.1 Installing Docker
    1.2.2 Building and Pulling CRISPRme Docker Image
2 Usage
  2.1 Directory Structure
  2.2 CRISPRme Functions
     2.2.1 Complete Search
     2.2.2 Complete Test
     2.2.3 Targets Integration
     2.2.4 GNOMAD Converter
     2.2.5 Generate Personal Card
     2.2.6 Web Interface
3 Test
  3.1 Quick Test
  3.2 Detailed Test
     3.2.1 Single Chromosome Test
     3.2.2 Full Genome Test
4 Citation
5 Contacts
6 License

0 System Requirements

To ensure optimal performance, CRISPRme requires the following:

  • Minimum Memory (RAM): 32 GB
    Suitable for typical use cases and smaller datasets.

  • Recommended Memory for Large Analyses: 64 GB or more
    Necessary for intensive operations such as whole-genome searches and processing large variant datasets.

For best results, confirm that your system meets or exceeds these specifications before running CRISPRme.

1 Installation

This section outlines the steps to install CRISPRme, tailored to suit different operating systems. Select the method that best matches your setup:

Each method ensures a streamlined and efficient installation, enabling you to use CRISPRme with minimal effort. Follow the detailed instructions provided in the respective sections below.

1.1 Install CRISPRme via Conda/Mamba

This section is organized into three subsections to guide you through the installation and maintenance of CRISPRme:

  • Installing Conda or Mamba:
    This subsection provides step-by-step instructions to install either Conda or Mamba. Begin here if you do not have these package managers installed on your machine.

  • Installing CRISPRme:
    Once you have Conda or Mamba set up, proceed to this subsection for detailed instructions on creating the CRISPRme environment and installing the necessary dependencies.

  • Updating CRISPRme:
    Learn how to update an existing CRISPRme installation to the latest version, ensuring access to new features and bug fixes.

1.1.1 Installing Conda or Mamba

Before installing CRISPRme, ensure you have either Conda or Mamba installed on your machine. Based on recommendations from the Bioconda community, we highly recommend using Mamba over Conda. Mamba is a faster, more efficient drop-in replacement for Conda, leveraging a high-performance dependency solver and components optimized in C++.

Step1: Install Conda or Mamba

Step 2: Configure Bioconda Channels

Once Mamba is installed, configure it to use Bioconda and related channels by running the following one-time setup commands:

mamba config --add channels bioconda
mamba config --add channels defaults
mamba config --add channels conda-forge
mamba config --set channel_priority strict

Note: If you prefer to use Conda, replace mamba with conda in the commands above

By completing these steps, your system will be fully prepared for installing CRISPRme.

1.1.2 Installing CRISPRme

We strongly recommend using Mamba to create CRISPRme's conda environment due to its superior speed and reliability in dependency management. However, if you prefer Conda, you can replace mamba with conda in all the commands below.

Step 1: Create CRISPRme's Environment

Open a terminal and execute the following command:

mamba create -n crisprme python=3.9 crisprme -y  # Install CRISPRme and its dependencies

This command sets up a dedicated conda environment named crisprme, installing CRISPRme along with all required dependencies.

Step 2: Activate the Environment

To activate the newly created CRISPRme environment, type:

mamba activate crisprme  # Enable the CRISPRme environment

Step 3: Test the Installation

To verify that CRISPRme is correctly installed, run the following commands in your terminal:

crisprme.py --version  # Display the installed CRISPRme version
crisprme.py            # List CRISPRme functionalities
  • The first command will output the version of CRISPRme (e.g., 2.1.6).
  • The second command should display CRISPRme's functionalities.

If both commands execute successfully, your installation is complete, and CRISPRme is ready to use.

1.1.3 Updating CRISPRme

To update an existing CRISPRme installation using Mamba or Conda, follow the steps below:

Step 1: Check the Latest Version

Visit the CRISPRme README to identify the latest version of the tool.

Step 2: Update CRISPRme

Run the following command in your terminal, replacing <latest_version> with the desired version number:

mamba install crisprme=<latest_version>  # Update CRISPRme to the specified version

For example, to update CRISPRme to version 2.1.6, execute:

mamba install crisprme=2.1.6

If you're using Conda, replace mamba with conda in the commands above.

Step 3: Verify the Update

After the update completes, ensure the installation was successful by checking the version:

crisprme.py --version  # Confirm the installed version

If the displayed version matches the one you installed, the update was successful.

1.2 Install CRISPRme via Docker

This section is organized into two subsections to guide you through the setup of CRISPRme using Docker:

  • Installing Docker:
    Provides step-by-step instructions for installing Docker on your system, ensuring compatibility with all operating systems, including Linux, macOS, and Windows.

  • Building and Pulling CRISPRme Docker Image:
    Explains how to create or download the CRISPRme Docker image to set up a containerized environment for seamless execution.

Follow the subsections in order if Docker is not yet installed on your machine. If Docker is already installed, skip to the second subsection.

1.2.1 Installing Docker

MacOS and Windows users are encouraged to install Docker to use CRISPRme. Linux users may also choose Docker for convenience and compatibility.

Docker provides tailored distributions for different operating systems. Follow the official Docker installation guide specific to your OS:

Linux-Specific Post-Installation Steps

If you're using Linux, additional configuration steps are required:

  1. Create the Docker Group:
sudo groupadd docker
  1. Add Your User to the Docker Group:
sudo usermod -aG docker $USER

Repeat this command for any additional users you want to include in the Docker Group.

  1. Restart Your Machine
    Log out and log back in, or restart your machine to apply the changes.

Testing Docker Installation

Once Docker is installed, verify the setup by opening a terminal window and typing:

docker run hello-world

If Docker is installed correctly, you should see output like this:

Hello from Docker!
This message shows that your installation appears to be working correctly.

To generate this message, Docker took the following steps:
 1. The Docker client contacted the Docker daemon.
 2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
 3. The Docker daemon created a new container from that image, which runs the executable that produces this output.
 4. The Docker daemon streamed this output to the Docker client, which displayed it on your terminal.

For more examples and ideas, visit:
 https://docs.docker.com/get-started/

1.2.2 Building and Pulling CRISPRme Docker Image

After installing Docker, you can download and build the CRISPRme Docker image by running the following command in a terminal:

docker pull pinellolab/crisprme

This command retrieves the latest pre-built CRISPRme image from Docker Hub and sets it up on your system, ensuring all required dependencies and configurations are included.

Once the download is complete, the CRISPRme Docker image will be ready for use. To confirm the image is successfully installed, you can list all available Docker images by typing:

docker images

Look for an entry similar to the following:

REPOSITORY          TAG       IMAGE ID       CREATED        SIZE
pinellolab/crisprme latest    <image_id>     <timestamp>    <size>

You are now ready to run CRISPRme using Docker.

2 Usage

CRISPRme is a tool designed for variant- and haplotype-aware CRISPR off-target analysis. It integrates robust functionalities for off-target detection, variant-aware search, and result analysis. The tool also includes a user-friendly graphical interface, which can be deployed locally to streamline its usage.

2.1 Directory Structure

CRISPRme operates within a specific directory structure to manage input data and outputs efficiently. To ensure proper functionality, your working directory must include the following main subdirectories:

  • Genomes

    • Purpose: Stores reference genomes.
    • Structure: Each reference genome resides in its own subdirectory.
    • Requirements: The genome must be split into separate files, each representing a single chromosome.

  • VCFs

    • Purpose: Contains variant data in VCF format.
    • Structure: Similar to the Genomes directory, each dataset has a dedicated subdirectory with VCF files split by chromosome.
    • Requirements: Files must be compressed using bgzip (with a .gz extension).

  • sampleIDs

    • Purpose: Lists the sample identifiers corresponding to the VCF datasets.
    • Structure: Tab-separated files, one for each VCF dataset, specifying the sample IDs.

  • Annotations

    • Purpose: Provides genome annotation data.
    • Format: Annotation files must be in BED format.

  • PAMs

    • Purpose: Specifies the Protospacer Adjacent Motif (PAM) sequences for off-target search.
    • Format: Text files containing PAM sequences.

The directory organization required by CRISPRme is illustrated below:

crisprme_dirtree.png

2.2 CRISPRme Functions

This section provides a comprehensive overview of CRISPRme's core functions, detailing each feature, the required input data and formats, and the resulting outputs. The following is a summary of CRISPRme's key features:

  • Complete Search (complete-search)
    Executes a genome-wide off-targets search across both reference and variant datasets (if specified), conducts Cutting Frequency Determination (CFD) and CRISTA analyses (if applicable), and identifies candidate targets.

  • Complete Test (complete-test)
    Tests CRISPRme pipeline on a small input dataset or the full genome, enabling users to validate the tool's functionality before performing large-scale analyses.

  • Targets Integration (targets-integration)
    Combines in silico predicted targets with experimental data to create a finalized target panel.

  • GNOMAD Converter (gnomAD-converter)
    Transforms GNOMAD VCFs (vcf.bgz format) into a format compatible with CRISPRme. The function supports VCFs from GNOMAD v3.1, v4.0, and v4.1, including joint VCFs.

  • Generate Personal Card (generate-personal-card)
    Generates a personalized summary for a specific sample, identifying all private off-targets unique to that individual.

  • Web Interface (web-interface)
    Launches CRISPRme's interactive web interface, allowing users to manage and execute tasks directly via a local browser.

2.2.1 Complete Search

The Complete Search function performs an exhaustive variant- and haplotype-aware off-target analysis, leveraging the provided reference genome and variant datasets to deliver comprehensive results. This feature integrates all critical stages of the CRISPRme pipeline, encompassing off-target identification, functional annotation, and detailed reporting.

Key highlights of the Complete Search functionality include:

  • Variant- and Haplotype-Awareness
    Accurately incorporates genetic variation, including population- and sample-specific variants, and haplotypes data, to identify off-targets that reflect real-world genomic diversity.

  • Comprehensive Off-Target Discovery
    Searches both the reference genome and user-specified variant datasets for potential off-targets, including those encompassing mismatches and bulges.

  • Functional Annotation
    Annotates off-targets with relevant genomic features, such as coding/non-coding regions, regulatory elements, and gene proximity.

  • Detailed Reporting
    Generates population-specific and sample-specific off-target summaries, highlighting variations that may impact specificity or introduce novel PAM sites. Provides CFD (Cutting Frequency Determination) and CRISTA scores, and mismatches and bulges counts to rank off-targets based on their potential impact. Includes graphical representations of findings to facilitate result interpretation.

  • Output Formats
    Produces user-friendly output files, including text-based tables and visualization-ready graphical summaries.

Usage Example for the Complete Search function:

  • Via Conda/Mamba

    crisprme.py complete-search \
  --genome Genomes/hg38 \  # reference genome directory
  --vcf vcf_config.1000G.HGDP.txt \  # config file declaring usage of 1000G and HGDP variant datasets
  --guide sg1617.txt \  # guide 
  --pam PAMs/20bp-NGG-spCas9.txt \  # NGG PAM file
  --annotation Annotations/dhs+gencode+encode.hg38.bed \  # annotation BED
  --gene_annotation Annotations/gencode.protein_coding.bed \  # gene proximity annotation BED
  --samplesID samplesIDs.1000G.HGDP.txt \  # config file declaring usage of 1000G and HGDP samples
  --be-window 4,8 \  # base editing window start and stop positions within off-targets
  --be-base A,G \  # nucleotide to test base editing potential (A>G)
  --mm 6 \  # number of max mismatches
  --bDNA 2 \  # number of max DNA bulges
  --bRNA 2 \  # number of max RNA bulges
  --merge 3 \  # merge off-targets mapped within 3 bp in clusters
  --sorting-criteria-scoring mm+bulges \  # prioritize within each cluster off-targets with highest score and lowest mm+bulges (CFD and CRISTA reports only)
  --sorting-criteria mm,bulges \  # prioritize within each cluster off-targets with lowest mm and bulges counts
  --output sg1617-NGG-1000G-HGDP \  # output directory name
  --thread 8  # number of threads

  • Via Docker

    docker run -v ${PWD}:/DATA -w /DATA -i pinellolab/crisprme \
  crisprme.py complete-search \
  --genome Genomes/hg38 \  # reference genome directory
  --vcf vcf_config.1000G.HGDP.txt \  # config file declaring usage of 1000G and HGDP variant datasets
  --guide sg1617.txt \  # guide 
  --pam PAMs/20bp-NGG-spCas9.txt \  # NGG PAM file
  --annotation Annotations/dhs+gencode+encode.hg38.bed \  # annotation BED
  --gene_annotation Annotations/gencode.protein_coding.bed \  # gene proximity annotation BED
  --samplesID samplesIDs.1000G.HGDP.txt \  # config file declaring usage of 1000G and HGDP samples
  --be-window 4,8 \  # base editing window start and stop positions within off-targets
  --be-base A,G \  # nucleotide to test base editing potential (A>G)
  --mm 6 \  # number of max mismatches
  --bDNA 2 \  # number of max DNA bulges
  --bRNA 2 \  # number of max RNA bulges
  --merge 3 \  # merge off-targets mapped within 3 bp in clusters
  --sorting-criteria-scoring mm+bulges \  # prioritize within each cluster off-targets with highest score and lowest mm+bulges (CFD and CRISTA reports only)
  --sorting-criteria mm,bulges \  # prioritize within each cluster off-targets with lowest mm and bulges counts
  --output sg1617-NGG-1000G-HGDP \  # output directory name
  --thread 8  # number of threads
Input Arguments

Below is a detailed list of the input arguments required or optionally used by the Complete Search function. Each parameter is explained to ensure clarity in its purpose and usage:

General Parameters

  • --help
    Displays the help message with usage details and exits. Useful for quickly referencing all available options.

  • --output (Required)
    Specifies the name of the output directory where all results from the analysis will be saved. This directory will be created within the Results directory.

  • --thread (Optional - Default: 4)
    Defines the number of CPU threads to use for parallel computation. Increasing the number of threads can speed up analysis on systems with multiple cores.

  • --debug (Optional)
    Runs the tool in debug mode.

Input Data Parameters

  • --genome (Required)
    Path to the directory containing the reference genome in FASTA format. Each chromosome must be in a separate file (e.g., chr1.fa, chr2.fa, etc.).

  • --vcf (Optional)
    Path to text config file listing the directories containing VCF files to be integrated into the analysis. When provided, CRISPRme conducts variant- and haplotype-aware searches. If not specified, the tool searches only on the reference genome.

  • --guide
    Path to a text file containing one or more guide RNA sequences (one per line) to search for in the input genome and variants. This argument cannot be used together with --sequence.

  • --sequence
    Path to a FASTA file listing guide RNA sequences. This argument is an alternative to --guide and cannot be used simultaneously.

  • --pam (Required)
    Path to a text file specifying the PAM sequence(s) required for the search. The file should define the PAM format (e.g., NGG for SpCas9).

Annotation Parameters (Optional)

  • --annotation
    Path to a BED file containing genomic annotations, such as regulatory regions (e.g., DNase hypersensitive sites, enhancers, promoters). These annotations provide functional context for identified off-targets.

  • --gene_annotation
    Path to a BED file containing gene information, such as Gencode protein-coding annotations. This is used to calculate the proximity of off-targets to genes for downstream analyses.

Base Editing Parameters (Optional)

  • --be-window
    Specifies the editing window for base editors, defined as start and stop positions relative to the guide RNA (1-based, comma-separated). This defines the region of interest for base-editing analysis.

  • --be-base
    Defines the target nucleotide(s) for base editing. This is only used when base editing functionality is needed.

Sample-Specific Parameters (Optional)

  • --samplesID
    Path to a text config file listing sample identifiers (one per line) corresponding to VCF datasets. This enables sample-specific off-target analyses. Mandatory if --vcf is specified.

Search and Merging Parameters

  • --mm (Required)
    Maximum number of mismatches allowed during off-target identification.

  • --bDNA (Required)
    Maximum allowable DNA bulge size.

  • --bRNA (Required)
    Maximum allowable RNA bulge size.

  • --merge (Optional - Default: 3)
    Defines the window size (in base pairs) used to merge closely spaced off-targets. Pivot targets are selected based on the highest score (e.g., CFD, CRISTA) or criteria defined by the --sorting-criteria.

  • --sorting-criteria-scoring (Optional - Default: mm+bulges)
    Specifies sorting criteria for merging when using CFD/CRISTA scores. Options include:

    • mm: Number of mismatches.
    • bulges: Total bulge size.
    • mm+bulges: Combined mismatches and bulges.

  • --sorting-criteria (Optional - Default: mm+bulges,mm)
    Sorting criteria used when CFD/CRISTA scores are unavailable. Options are similar to --sorting-criteria-scoring but tailored for simpler analyses.

Note 1: Ensure compatibility between input files and genome builds (e.g., hg38 or hg19) to avoid alignment issues.

Note 2: Optional arguments can be omitted when not applicable, but required arguments must always be specified.

Output Data Overview

The Complete Search function generates a comprehensive suite of reports, detailing the identified and prioritized targets, along with statistical and graphical summaries. These outputs are essential for interpreting the search results and understanding the impact of genetic diversity on CRISPR off-target.

Output Off-targets Files Description

  1. *.integrated_results.tsv

    • Contents: A detailed file containing the top targets (*.bestMerge.txt) enriched with annotations from the input files. Includes:

      • Gene proximity of off-targets
      • Overlaps with regulatory elements

    • Purpose: Integrates functional genomic context into the prioritization of off-targets.

  2. *.all_results_with_alternative_alignments.tsv

    • Contents: Comprehensive listing of all identified targets, including alternative alignments. Annotated with:

      • Gene proximity
      • Overlaps with regulatory elements

    • Purpose: Facilitates a full exploration of CRISPRme's off-target predictions and their functional relevance.

Guide-Specific Summary Files

These files summarize off-target statistics per guide sequence based on different sorting criteria.

  1. *.summary_by_guide.<guide-sequence>_CFD.txt

    • Contents: Summarizes off-target counts per guide using the CFD score as the primary sorting criterion (data derived from *.integrated_results.tsv). Includes counts of:

      • Targets by bulge type (DNA, RNA).
      • Mismatch number and bulge size.
      • Targets in the reference genome, variant genome, and those caused by PAM creation due to variants.

    • Purpose: Provides insight into the distribution and characteristics of off-targets prioritized by CFD score.

  2. *.summary_by_guide.<guide-sequence>_CRISTA.txt

    • Contents: Summarizes off-target counts per guide using the CRISTA score as the primary sorting criterion (data derived from *.integrated_results.tsv). Includes counts of:

      • Targets by bulge type (DNA, RNA).
      • Mismatch number and bulge size.
      • Targets in the reference genome, variant genome, and those caused by PAM creation due to variants.

    • Purpose: Provides insight into the distribution and characteristics of off-targets prioritized by CRISTA score.

  3. *.summary_by_guide.<guide-sequence>_fewest.txt

    • Contents: Summarizes off-target counts per guide using the fewest mismatches and bulges as the sorting criterion.

    • Purpose: Highlights off-targets that are closest to perfect matches, providing an alternative prioritization method.

Sample-Specific Summary Files

These files focus on off-targets unique to individual samples and their populations.

  1. *.summary_by_samples.<guide-sequence>_CFD.txt

    • Contents: Counts of private off-targets per sample, sorted by CFD score. Reports targets:

      • Private to the sample.
      • Found in the population or superpopulation.
      • Resulting from PAM creation due to a variant.

    • Purpose: Quantifies sample-specific off-targets and their broader population impact.

  2. *.summary_by_samples.<guide-sequence>_CRISTA.txt

    • Contents: Similar to the CFD-based sample summary but uses CRISTA score for sorting.

  3. *.summary_by_samples.<guide-sequence>_fewest.txt

    • Contents: Summarizes private off-targets using the fewest mismatches and bulges as the sorting criterion.

Graphical Output

  1. imgs directory

    • Contents: Contains visual representations of the top 1000 targets based on CFD score, CRISTA score, and Fewest mismatches and bulges. Images include:

      • Bar plots showing the distribution of targets across populations and bulge types.
      • Graphical summaries illustrating the impact of genetic variants on mismatches, bulge size, and scores.

    • Purpose: Facilitates easy interpretation and presentation of CRISPRme results.

2.2.2 Complete Test

The Complete Test module provides an automated pipeline for verifying the correct installation and functionality of CRISPRme. This feature is designed to simplify the validation process by automatically setting up the required directory structure, downloading essential files, and offering flexible testing options tailored to different user needs.

This function automatically creates the CRISPRme directory structure required for the tool to function properly. Furthermore, it downloads and prepares all necessary files for testing, ensuring that users do not need to manually manage dependencies. Complete Test module allows users to perform a test limited to a specific chromosome (e.g., chromosome 22), significantly reducing runtime and resource usage. However, is also available the option to test on the entire genome, ensuring all components of CRISPRme work correctly across large-scale datasets. This function supports testing with 1000 Genomes Phase 3 dataset and Human Genome Diversity Project (HGDP) dataset. Testing parameters, such as the genome dataset and test type, can be customized via command-line arguments, allowing users to tailor the testing process to their system capabilities and goals.

This module is suited for:

  • Initial Installation Verification: Test whether CRISPRme has been installed correctly and is functioning as expected before running actual analyses.

  • System Configuration Testing: Validate the compatibility of CRISPRme with different system configurations (e.g., varying thread counts, datasets).

  • Dataset Evaluation: Test specific datasets (1000 Genomes Phase 3 or HGDP) to confirm their suitability for the user’s research needs.

Usage Example for the Complete Test function:

  • Via Conda/Mamba

    crisprme.py complete-test \ 
  --chrom chr22 \  # test on chromosome 22 data only
  --vcf_dataset 1000G  # test using 1000G variants

  • Via Docker

    docker run -v ${PWD}:/DATA -w /DATA -i pinellolab/crisprme \ 
  crisprme.py complete-test \ 
  --chrom chr22 \  # test on chromosome 22 data only
  --vcf_dataset 1000G  # test using 1000G variants
Input Arguments

Below is a detailed list of the input arguments required by the Complete Test function, including detailed explanations and default behaviors:

General Parameters

  • --help
    Displays the help message with details about the available options and exits the program.

  • --thread (Optional - Default: 4)
    Specifies the number of threads to use for the computation, allowing for parallel processing.

  • --debug (Optional)
    Runs the tool in debug mode.

Input Data Parameters (Optional)

  • --chrom
    Specifies the chromosome to be used in the CRISPRme test. The chromosome identifier must be prefixed with chr (e.g., chr22). When this argument is provided, the test will be limited to the specified chromosome, enabling faster execution for targeted validation. Default behavior: Run the test across the entire genome.

  • --vcf_dataset
    Defines the variant dataset to be utilized for testing CRISPRme. Available options include:

    • 1000G: Uses the 1000 Genomes Phase 3 dataset.

    • HGDP: Uses the Human Genome Diversity Project dataset.

    Default behavior: Use 1000 Genomes variant data.

Output Data Overview

The results generated by the Complete Test function are saved in a subdirectory within the Results directory, named crisprme-test-* (with * replaced by a unique identifier).

The output folder contains the same set of files produced by the Complete Search functionality, including:

  • Detailed target reports that prioritize off-target candidates based on various criteria.

  • Summaries categorized by guides and samples.

  • Graphical representations of results, such as bar plots and impact assessments of genetic variants on target metrics.

For a detailed description of the contents of these files, please refer to the corresponding subsection in Complete Search.

2.2.3 Targets Integration

The Targets Integration function enables the seamless combination of computationally predicted off-targets identified by CRISPRme with experimentally validated off-targets, such as those obtained from GUIDE-seq, CIRCLE-seq, or other high-throughput methods. This integration enhances the interpretability and reliability of CRISPRme’s results by merging empirical data with in silico predictions.

This module supports integration with user-provided datasets in BED format, enabling flexibility in validation sources. Targets Integration

Usage Example for the Complete Test function:

  • Via Conda/Mamba

    crisprme.py targets-integration \ 
  --targets results.integrated_results.tsv \  # search results
  --empirical_data empirical_data.bed \  # empirical data BED 
  --output integrated_targets_dir  # output directory

  • Via Docker

    docker run -v ${PWD}:/DATA -w /DATA -i i pinellolab/crisprme \
  crisprme.py targets-integration \ 
  --targets results.integrated_results.tsv \  # search results
  --empirical_data empirical_data.bed \  # empirical data BED 
  --output integrated_targets_dir  # output directory
Input Arguments

Below is a detailed list of the input arguments required by the Target Integration function, including detailed explanations and default behaviors:

General Parameters

  • --help
    Displays the help message with details about the available options and exits the program.

  • --debug (Optional)
    Runs the tool in debug mode.

Input Data Parameters

  • --targets (Required)
    Specifies the file containing targets identified and processed from a CRISPRme search. This file should include predicted off-target data such as mismatch counts, bulge sizes, and scores (e.g., CFD, CRISTA).

  • --empirical_data (Required)
    Path to a BED file containing empirically validated off-targets. This file typically includes genomic coordinates and additional metadata from experiments such as GUIDE-seq or CIRCLE-seq. Ensure that the BED file format adheres to standard conventions for compatibility.

  • --output (Required)
    Name of the directory where the resulting integrated targets file will be saved.

Output Data Overview

The Targets Integration module generates output files that merge computationally predicted targets from CRISPRme with experimentally validated off-targets provided in the BED file. The integrated data is stored in the specified output directory and includes the integrated_results.tsv. This tab-separated file contains the combined list of off-target sites. Each entry represents a merged record from CRISPRme predictions and empirical data, where overlaps are identified based on genomic coordinates.

2.2.4 GNOMAD Converter

The GNOMAD Converter function is a utility designed to preprocess and convert GNOMAD VCF files into a format compatible with CRISPRme for off-target analysis. This tool facilitates the inclusion of population-level genetic variation data from GNOMAD into CRISPRme.

The converter currently supports VCF files from the following GNOMAD versions:

  • v3.1
  • v4.0
  • v4.1, including joint VCF files (exomes + genomes).

Since individual sample data are not available in GNOMAD VCFs, the tool relies on population-level groupings. Populations are treated as individual "samples" for the purpose of conversion. This approach supports population-based statistical analyses in CRISPRme, such as identifying population-specific off-targets.

Note 1: For studies requiring sample-specific statistics, GNOMAD is not recommended due to its population-level nature.

Note 2: The GNOMAD Converter function is particularly useful for creating GNOMAD-compatible datasets formatted for CRISPRme's population-aware off-target analysis.

Note 3: Since GNOMAD provides population-level data rather than individual-level data, CRISPRme interprets populations as pseudo-individuals. This approach allows meaningful population-level statistics but is not suitable for applications requiring individual-level granularity.

To ensure a smooth conversion process, sample ID files compatible with GNOMAD VCFs must be provided. These files are available for download from the CRISPRme GitHub repository:

The conversion process preserves all variant information necessary for CRISPRme analyses, including allele frequencies and genotypes (if applicable).

Usage Example for the GNOMAD Converter function:

  • Via Conda/Mamba

    crisprme.py gnomAD-converter \
  --gnomAD_VCFdir gnomad_vcf_dir \  # directory containing GNOMAD VCFs
  --samplesID samplesIDs.gnomad.v41.txt \  # GNOMAD v4.1 samples file
  --keep \  # keep variants with filter different from PASS
  --thread 4  # number of threads

  • Via Docker

    docker run -v ${PWD}:/DATA -w /DATA -i pinellolab/crisprme \
  crisprme.py gnomAD-converter \
  --gnomAD_VCFdir gnomad_vcf_dir \  # directory containing GNOMAD VCFs
  --samplesID samplesIDs.gnomad.v41.txt \  # GNOMAD v4.1 samples file
  --keep \  # keep variants with filter different from PASS
  --thread 4  # number of threads
Input Arguments

Below is a detailed list of the input arguments required by the GNOMAD Converter function, including detailed explanations and default behaviors:

General Parameters

  • --help
    Displays the help message with details about the available options and exits the program.

  • --thread (Optional - Default: 4)
    Specifies the number of threads to use for the computation, allowing for parallel processing.

  • --debug (Optional)
    Runs the tool in debug mode.

Input Data Parameters

  • --gnomAD_VCFdir (Required)
    Specifies the directory containing the gnomAD VCF files to be processed and converted into a format compatible with CRISPRme.

  • --samplesID (Required)
    Path to a text file containing the sample IDs used during the conversion process. In this file, GNOMAD populations are treated as pseudo-individual samples to create population-based VCFs for CRISPRme.

  • --joint (Optional)
    Use this flag if GNOMAD VCFs being processed are joint VCFs, such as GNOMAD v4.1 joint variant files. Default Behavior: Assumes the input VCFs are not joint.

  • --keep (Optional)
    Use this flag to retain all variants during the conversion, regardless of the FILTER field value. Default behavior: Excludes variants that do not have a PASS value in the FILTER field.

  • --multiallelic (Optional)
    Indicates whether to merge variants at the same genomic position into a single multiallelic record. Default behavior: Keeps variants as biallelic and does not merge them.

Output Data Overview

The output of the GNOMAD Converter function consists of converted VCF files formatted to be compatible with CRISPRme. These files are stored in the same directory as the input VCF files. The conversion process ensures that the output adheres to CRISPRme's input specifications for population-level analysis. Below are details about the generated data:

File Naming Conventions

  • Multiallelic Variant Merging (--multiallelic)
    If multiallelic entries were generated by merging variants at the same position, the filename will include a tag such as *.multiallelic.*, *.biallelic.* otherwise.

  • Joint Variant Files (--joint)
    If joint VCFs were processed, the filenames will be labeled accordingly, for example, *.joint.*.

Content of the Converted VCFs

  • Population Representation
    Each output VCF treats GNOMAD populations as pseudo-individual samples, enabling CRISPRme to perform population-based statistical analysis. This structure is reflected in the sample columns of the output VCFs.

  • Variant Quality
    If the --keep flag is used, all variants from the input VCF are included, regardless of their quality as indicated in the FILTER field. Without the --keep flag, only variants with a PASS in the FILTER field are retained.

  • Allele Representation
    By default, the converter preserves biallelic representation, creating one row per variant. If the --multiallelic flag is used, variants at the same position are merged into multiallelic entries.

  • Compatible Structure
    The output files are structured to align with CRISPRme's population-aware off-target analysis, ensuring seamless integration into the tool's pipeline.

2.2.5 Generate Personal Card

The Generate Personal Card functionality creates a sample-specific report, referred to as personal card. This report details all off-targets identified by CRISPRme for a given sample's unique genomic sequence. The report accounts for private variants (genetic differences specific to the sample not shared with other populations or individuals) and their potential impact on off-target editing outcomes.

This feature is particularly useful in scenarios where personalized gene-editing strategies are required, such as analyzing how private genetic variations influence the efficacy and safety of CRISPR-based interventions. The personal card provides the following key insights:

  • Identification of off-target sequences present exclusively in the sample due to private variants. This allows researchers to evaluate potential risks or opportunities unique to the individual’s genome.

  • Detailed information for each input guide, showing how private variants affect off-target sequences.

This functionality is a critical tool for advancing personalized medicine and precision genome editing, enabling researchers and clinicians to tailor CRISPR-based solutions to an individual’s unique genetic makeup.

Usage Example for the Generate Personal Card function:

  • Via Conda/Mamba

    crisprme.py generate-personal-card \
  --result_dir Results/sg1617.6.2.2 \  # results directory from previous search
  --guide_seq CTAACAGTTGCTTTTATCACNNN \  # guide sequence 
  --sample_id NA21129  # sample ID

  • Via Docker

    docker run -v ${PWD}:/DATA -w /DATA -i i pinellolab/crisprme \
  crisprme.py generate-personal-card \
  --result_dir Results/sg1617.6.2.2 \  # results directory from previous search
  --guide_seq CTAACAGTTGCTTTTATCACNNN \  # guide sequence 
  --sample_id NA21129  # sample ID
Input Arguments

Below is a detailed list of the input arguments required by the Generate Personal Card function, including detailed explanations and default behaviors:

General Parameters

  • --help
    Displays the help message with details about the available options and exits the program.

  • --debug (Optional)
    Runs the tool in debug mode.

Input Data Parameters

  • --results_dir (Required)
    Specifies the directory containing the CRISPRme search results. The tool extracts the relevant targets from the reports available in this directory. Ensure this path includes all necessary output files generated by CRISPRme during the analysis (*.integrated_results.*).

  • --guide_seq (Required)
    The sgRNA sequence for which the sample-specific targets will be extracted. This argument ensures that the generated personal card is tailored to a specific guide of interest, enabling targeted analysis.

  • --sample_id (Required)
    The identifier of the sample for which the personal card will be created. This ID corresponds to the unique genetic profile being analyzed. Ensure the sample ID matches the format used in the input data to avoid discrepancies.

Output Data Overview

The Generate Personal Card functionality produces sample-specific outputs, allowing researchers to assess how private genetic variants influence CRISPR off-target activity.

Note 1: All output files include the sample ID in their file names for easy identification and traceability (e.g., *.<sample_id>.*).

Note 2: Outputs are tailored to the specified guide sequence and sample ID, ensuring precise and personalized reporting.

Output Off-targets Files description

  • *.<sample_id>.<guide_seq>.private_targets.tsv
    A detailed table reporting all off-target sequences specific to the sample. Extracted from the .integrated_results. file within the input directory. The report is generated in the results directory specified via --result_dir.

Graphical Output

  • imgs directory
    The function generates plots illustrating the effect of private genetic variants on the sample-specific targets. Displays changes in the CFD and
    CRISTA scores, and number of Mismatches and Bulges highlighting off-target risk influenced by genetic variants.

2.2.6 Web Interface

The Web Interface module offers a user-friendly, locally hosted graphical user interface (GUI) for CRISPRme. This feature replicates the functionality of CRISPRme's online platform, enabling users to execute CRISPRme workflows and explore search results interactively without requiring an internet connection.

The GUI allows users to submit CRISPRme jobs directly through the web interface. Users can upload input files, configure parameters, and monitor progress with ease.

The interface provides an intuitive way to explore the output files generated by CRISPRme. Users can filter targets by criteria such as mismatch count, bulge size, or scores (e.g., CFD or CRISTA). The interface includes dynamic plots and charts. Results are presented in a structured, easy-to-navigate format, linking data to relevant genomic annotations. The web interface runs as a local server, ensuring data privacy and fast response times. Users can access it via their preferred web browser, with compatibility confirmed for: Google Chrome, Mozilla Firefox, and Safari. All functionalities are self-contained, eliminating the need for an internet connection. This is particularly useful for secure environments or systems without reliable internet access.

Usage example for the Web Interface function:

  • Via Conda/Mamba

    crisprme.py web-interface  # Starts the local server and launches the web interface

  • Via Docker

    docker run -v ${PWD}:/DATA -w /DATA -i pinellolab/crisprme \
  crisprme.py web-interface  # Starts the local server and launches the web interface
Input Arguments

Below is a detailed list of the input arguments required by the Web Interface function:

  • --help
    Displays the help message and exits.

  • --debug (Optional)
    Launches the local server in debug mode. This mode enables verbose logging, which is useful for troubleshooting and diagnosing issues during setup or operation. It provides detailed error messages and runtime information in the console.

Output Data Overview

This function does not generate output data in the form of files or reports. Instead, it serves to launch a local graphical user interface, which allows users to interactively explore and run CRISPRme analyses. All results are displayed dynamically within the web interface itself, offering an interactive experience for viewing CRISPRme data and outputs (see Section 2.2.1 for details).

3 Test

This section covers CRISPRme testing. This ensures that the software is correctly installed, configured, and ready for use. This section covers two testing options tailored to user needs:

  • Quick Test
    A lightweight test to verify the installation and basic functionality.

  • Detailed Test
    A comprehensive pipeline test, replicating a full-scale CRISPRme analysis.

For persistent issues, refer to the CRISPRme GitHub Issues Page or contact the maintainers.

3.1 Quick Test

The Quick Test provides a simple and efficient way to confirm that CRISPRme is correctly installed and operational on your system. It ensures that the software is accessible, dependencies are properly configured, and the version matches the expected release.

Step 1: Verify CRISPRme Installation

Open a terminal and execute the following command to check the software version:

  • Via Conda/Mamba

    crisprme.py --version  # Expected output: v2.1.6

  • Via Docker

    docker run -v ${PWD}:/DATA -w /DATA -i pinellolab/crisprme \
crisprme.py --version  # Expected output: v2.1.6

If the output displays the correct software version (e.g., v2.1.6), CRISPRme is successfully installed and ready for use.

Step 2: Access CRISPRme Help Menu

To explore the functionalities and input parameters of CRISPRme, use the --help flag:

  • Via Conda/Mamba 
    crisprme.py --help
  • Via Docker 
    docker run -v ${PWD}:/DATA -w /DATA -i pinellolab/crisprme \
  crisprme.py --help

The help menu provides detailed descriptions of CRISPRme's features and usage instructions, enabling users to familiarize themselves with available options and workflows.

Why Perform the Quick Test?

The Quick Test is highly recommended:

  1. Immediately after installation to ensure the setup is correct.

  2. Before running full-scale analyses to verify dependencies and environment configurations.

If the Quick Test completes without errors, you can confidently proceed to detailed analyses using CRISPRme’s powerful tools and features.

3.2 Detailed Test

For a more comprehensive validation of CRISPRme’s functionality, this detailed test offers a full-scale test of the main CRISPRme pipeline, specifically its Complete Search module. This test provides two distinct options for testing: a quicker, focused test on a single chromosome and a more detailed, exhaustive test across the entire genome:

  • Single Chromosome Test
    This test runs a search for potential off-targets on a single chromosome, such as chromosome 22, enriched with variants from the 1000 Genomes or Human Genome Diversity Project (HGDP) datasets. This provides a fast way to check CRISPRme's ability to process variant data and identify off-targets for a specific chromosome.

  • Full Genome Test
    This test checks CRISPRme's ability to search for potential off-targets across the entire human genome. It runs a search using the sg1617 guide RNA with an NGG PAM site, incorporating both Gencode and ENCODE annotations to ensure comprehensive results.

Why Perform the Detailed Test?

The Detailed Test is ideal for users who wish to:

  • Fully assess CRISPRme’s performance across the genome.
  • Perform a more comprehensive check on CRISPRme’s ability to handle large-scale data and complex analyses.

Successful completion of the detailed test confirms the full functionality of CRISPRme, ensuring it is ready to handle large datasets and complex genetic analysis tasks.

3.2.1 Single Chromosome Test

To run the quicker test on chromosome 22 using the 1000 Genomes dataset, execute the following commands:

  • Via Conda/Mamba

    crisprme.py complete-test \ 
  --chrom chr22 \
  --vcf_dataset 1000G  # to test on HGDP replace '1000G' with 'HGDP'

  • Via Docker

    docker run -v ${PWD}:/DATA -w /DATA -i pinellolab/crisprme \
  crisprme.py complete-test \ 
  --chrom chr22 \
  --vcf_dataset 1000G  # to test on HGDP replace '1000G' with 'HGDP'

3.2.2 Full Genome Test

To run the detailed test across the entire genome using the 1000 Genomes variants, execute the following commands:

  • Via Conda/Mamba

    crisprme.py complete-test \ 
  --vcf_dataset 1000G  # to test on HGDP replace '1000G' with 'HGDP'

  • Via Docker

    docker run -v ${PWD}:/DATA -w /DATA -i pinellolab/crisprme \
  crisprme.py complete-test \ 
  --vcf_dataset 1000G  # to test on HGDP replace '1000G' with 'HGDP'

To run the test on a single chromosome (chromosome 22) and using 1000 Genomes variants, open a new terminal window and run the following command:

  • Via Conda/Mamba
crisprme.py complete-test --chrom chr22 --vcf_dataset 1000G
  • Via Docker:
docker run -v ${PWD}:/DATA -w /DATA -i i pinellolab/crisprme crisprme.py complete-test --chrom 22 --vcf_dataset 1000G

To run the test on the entire genome and using 1000 Genomes variants, open a new terminal window and run the following command:

  • Via Conda/Mamba
crisprme.py complete-test --vcf_dataset 1000G
  • Via Docker:
docker run -v ${PWD}:/DATA -w /DATA -i i pinellolab/crisprme crisprme.py complete-test --vcf_dataset 1000G

4 Citation

If you use CRISPRme in your research, please cite our paper:

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. Epub 2022 Dec 15. PMID: 36522432; PMCID: PMC10272994.

5 Contacts

6 License

CRISPRme is licensed under the AGPL-3.0 license, which permits its use for academic research purposes only.

For any commercial or for-profit use, a separate license must be obtained. For further information regarding licensing for commercial purposes, please contact Luca Pinello at lpinello@mgh.harvard.edu.

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