Snakemake workflow: CRISPRDesigner

This snakemake workflow is a wrapper for the CRISPR SpCas9 guideRNA designer used in Engreitz Lab CRISPR screens (e.g., Fulco et al. Science 2016, Fulco, Nasser, et al. Nat Genet 2019).

Authors

• Jesse Engreitz (@engreitz)

Description

Guide RNAs are first designed against all NGG PAM sites.

Guide specificity / off-target scores are calculated according to the original Feng Zhang MIT score, and efficacy scores are computed using Tim Wang et al. Science 2014.

Then, the following filters are applied to remove gRNAs that match any of the following conditions:

• A N in the reference genome
• More than 1 T/U in the last 4 nucleotides, which combine with the first few nucleotides of the gRNA scaffold to terminate Pol III transcription
• More than 40% total T/U content
• 4-mer T/U repeats
• 5-mer mononucleotide repeats
• Low-complexity sequences, defined as 10 nucleotides of 2-nt repeat, 12 nucleotides of 3- or 4-nt repeats, or 18 nucleotides of 5- or 6-nt repeats
• <= 20% GC content

• = 90% GC content

This code also allows providing a list of previously designed and scored gRNAs, so that new runs only need to design gRNAs for new regions.

Usage

Step 1: Clone this github repository

Clone this to your local system, into the place where you want to perform the data analysis.

Step 2: Install conda environment

Install Snakemake and conda environment using conda:

conda env create --file workflow/envs/CRISPRDesignerSnakemake.yaml

For installation details, see the instructions in the Snakemake documentation.

Step 3: Create input regions BED file

Create an input 'regions' BED file, with columns chr start end name. The snakemake script will find and score guides in these regions, and label them with the provided region name. If desired, also collect a list of pre-designed guideRNAs (BED file with regions, and filteredGuides.bed file with guide scores)

Step 4: Configure workflow

Configure the workflow according to your needs via editing the files in the config/ folder. Adjust config.yaml to configure the workflow execution, including to define the regions variable to point to your BED file. If desired, set up predesigned_guides in config.yaml.

Step 5: Execute workflow

Activate the conda environment:

conda activate CRISPRDesignerSnakemake

Test your configuration by performing a dry-run via

snakemake -n

Execute the workflow locally via

snakemake --cores $N

using $N cores or run it in a cluster environment via

snakemake \ --config java_memory=15g \ --cores 1 \ --jobs 50 \ --cluster "sbatch -n 1 -c 1 --mem 16G -t 12:00:00 -p engreitz -J CRISPRDesigner_{rule} -o logs/{rule}_{wildcards} -e logs/{rule}_{wildcards}"

For more about cluster configuration using snakemake, see here