CRISPRroots

CRISPRroots: CRISPR--Cas9-mediated edits with accompanying RNA-seq data assessed for on-target and off-target sites.

The CRISPR/Cas9 genome editing tool can be used to study genomic variants and gene knockouts. By combining CRISPR/Cas9 mediated editing with transcriptomic analyses it is possible to measure the effects of genome alterations on gene expression. In such experiments it is crucial to understand not only if the editing was successful but also if the observed differential gene expression is the result of intended genome edits and not that of unwanted off-target effects. Potential off-targets sites that CRISPR/Cas9 may have edited need therefore to be verified by sequencing. However, a CRISPR/Cas9 gRNA may have hundreds (or thousands) potential off-target binding sites in a genome. How to decide which of them should be validated with higher priority? The RNA-seq data that can be sequenced as part of a CRISPR/Cas9 experiment contains information about the sequence and expression level of potential off-target sites/genes located in transcribed regions. Here we preset CRISPRroots, a method that combines CRISPR/Cas9 and guide RNA binding properties, gene expression changes, and sequence variants between edited and non-edited cells, to discover and rank potential off-targets. The method is described in the corresponding publication (see below).

Installation and configuration

Prerequisites

The CRISPRroots pipeline can be executed via Snakemake.
To be executed in Sankemake, the pipeline requires:

• conda : ≥4.8.5
• snakemake : ≥5.32.00

All other software requirements are satisfied by the Conda environments defined in Snakemake, which are installed by starting Snakemake with the --use-conda flag.

The pipeline was tested in a x86 64 GNU/Linux environment with Ubuntu v.18.04.1 (or newer) installed.

Setup

CRISPRroots is available to download from https://rth.dk/resources/crispr/.
A test dataset is also available on the same website. After downloading and un-packing the software and the test dataset, we recommend to explore the directory structure of the test dataset.

• CRISPRroots_test_dataset
  • resources #reference files
  • QPRT_DEL268T_chr16_10M-40M #sample directory
  • make_config.py #setup config file

the script make_config.py can be used to automatically set the paths to the data, resources, and code directories in the configuration file config.yaml located in CRISPRroots_test_dataset/QPRT_DEL268T_chr16_10M-40M. To run the script, you need python3. To setup the config file for the test dataset, run:

  cd CRISPRroots_test_dataset
  python3 make_config.py --CRISPRroots <path_to_CRISPRroots>

The config file contains the parameters defined for the execution of the various steps of the pipeline. A copy of the config file for the CRISPRroots test dataset is provided together with the CRISPRroots software package. This file can be used as template to create the configuration file for your own dataset.

For a complete list of parameters, options, and usage examples for CRISPRroots please read the CRISPRroots_Manual.pdf included in the CRISPRroots software folder.

Pipeline usage

Basic Usage

Assuming you have installed the software in the prerequisites, you can run the pipeline from within the directory containing the config file (in the test dataset this is the subfolder QPRT_DEL268T_chr16_10M-40M) as follows:

  cd QPRT_DEL268T_chr16_10M-40M #for usage in the test dataset, substitute with your own directory otherwise
  snakemake -s <path_to_CRISPRroots>/run.smk --use-conda --dry-run 
  snakemake -s <path_to_CRISPRroots>/run.smk --use-conda --cores <int>

We recommend to first run Snakemake with --dryrun.
This displays what will be done without executing it and highlights if any input file is missing. In the commands above, --cores specifies the maximum number of cores used in parallel by Snakemake. The pre-computed results/reports for the test dataset are available in the folder CRISPRroots_test_dataset/QPRT_DEL268T_chr16_10M-40M/pre-computed.

Hint: Snakemake allows to visualize the jobs as a graph (directed acyclic graph, or DAG), highlighting the jobs completed and those to be run in different ways. To create an svg plot of your DAG, run the following command:

    snakemake -s <path_to_CRISPRroots>/run.smk --dag | dot -Tsvg > dag.svg

To learn more about the DAG and the visualization of jobs please visit the Snakemake tutorial at https://snakemake.readthedocs.io/en/stable/tutorial/basics.html (Step 4: Indexing read alignments and visualizing the DAG of jobs).

Advanced usage

The pipeline can also be used to accomplish only specific tasks. For example, to only perform the the pre-processing of the dataset and read mapping, you can run CRISPRroots with the rule flag preproc_and_map at the end:

  snakemake -s <path_to_CRISPRroots/run.smk --use-conda --cores <int> preproc_and_map

The tasks (Snakemake rules) ready for use are the following:

• variants_to_genome: executes the rules necessary to produce files containing filtered variants between each sample and the reference genome. Output in: <path_to_results_folder>/6_GATK_variants/<sample name>/variants_filtered.vcf
• eSNPKaryotyping: Executes the R package eSNP-Karyotyping for the analysis of genome integrity from RNA-seq. The standard workflow is modified to employ reads mapped with STAR instead of TopHat2. Output in: <path_to_report_folder>/eSNPKaryotyping/
• on_target_check: executes the on-target editing assessment. Output in: <path_to_report_folder>/on_target_knockin.xlsx; <path_to_report_folder>/on_target_knockout.xlsx
• get_variated_genome: produces a variant-aware version of the reference genome, in which variants discovered from the RNA-seq are introduced in the reference sequence. Output in: <path_to_results_folder>/6_GATK_variants/variated_genome.fa
• get_lib_type: assesses the library type with RSeQC. Output in: <path_to_results_folder>/2-1_RSeQC_libtype/
• preproc_and_map: runs the reads pre-processing and mapping.
  Mapping output in: <path_to_results_folder>/2_sortaligned/
  Mapping statistics in: <path_to_report_folder>/report/mapping_stats.xlsx
  Pre-processing results in: <path_to_results_folder>/preproc/
  Pre-processing statistics in: <path_to_report_folder>/report/multiqc_samples_stats.xlsx

NB: In the test dataset, <path_to_results_folder> and <path_to_report_folder> correspond to the subfolders results and report in that will be generated inside CRISPRroots_test_dataset/QPRT_DEL268T_chr16_10M-40M/ after completing the pipeline.

A useful flag for the execution of specific tasks is --notemp. This avoids removing output files defined as temporary in the pipeline (e.g. partially processed reads). It can be convenient to use it when executing only a part of the pipeline, to avoid the removal of temporary files that will need to be recreated if required by a subsequent execution of the pipeline.

Running the pipeline in a computer cluster

CRISPRroots can also be launched on a computer cluster. An example of how to set up CRISPRroots to run it with the Slurm Workload Manager is given in the test directory and can be used as:

  cd CRISPRroots_test_dataset/QPRT_DEL268T_chr16_10M-40M
  ./<path_to_CRISPRroots>/cluster_run.sh

You can add the name of a target rule to run only a part of the pipeline as below:

  ./<path_to_CRISPRroots>/cluster_run.sh [target_rule]

Output files

The pipeline’s output files are collected in two folders: report and results. Additionally, Snakemake generates a hidden folder, .snakemake, at the moment it is executed. Here, Snakemake stores all the information necessary to track the activity of the pipeline and the origin of each file it generates. The conda environments created by Snakemake are also stored in this folder.

The report folder contains the main output, including the candidate off-targets and the knockin/knockout assessment. Results regarding differential expression and processing statistics (data quality and map- ping) are also present.

Please refer to the CRISPRroots_Manual.pdf included in the CRISPRroots software folder for a complete description fo the output and its content.

Copyright

Copyright 2021 by the contributors:

Giulia Corsi giulia@rth.dk, Veerendra Gadekar veer@rth.dk

GNU GENERAL PUBLIC LICENSE

This is a free software: you can redistribute it and/or modify it under the terms of the GNU General Public License, either version 3 of the License, or (at your option) any later version. See http://www.gnu.org/licenses/.

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 General Public License for more details.

Citations

If you use CRISPRroots in your publication please cite:
CRISPRroots: on- and off-target assessment of RNA-seq data in CRISPR-Cas9 edited cells
Corsi GI, Gadekar VP, Gorodkin J, Seemann SE. Nucleic Acids Research (2021, in press).

Contact

In case of problems or bug reports, please contact software+crisprroots@rth.dk