cansavvy/pgMAP_pipeline

pgMAP (paired guide RNA MAPper) Pipeline

From pgMAP: a pipeline to enable guide RNA read mapping from dual-targeting CRISPR screens

Running the pipeline

1. Clone or fork the git repo from https://github.com/FredHutch/pgMAP_pipeline/ (if you are not sure of the difference between cloning and forking, check out the explainer here. Make sure you are on the main branch.

2. First time only: Build a Conda environment for Snakemake (defined by workflow/envs/snakemake.yaml, for more detail see the Snakemake documentation). Running your analysis within snakemake_env will enable you to use the same version of Snakemake and all other supporting packages that were used to develop pgMAP. To build the snakemake_env, do one of the following:

   1. Install Mamba, which is essentially a faster version of Conda that is used to run Snakemake, by doing one of the following:
      • If you do not already have the Conda package manager installed, install Mambaforge
      • If you do already have Conda installed, run the following command: $ conda install -n base -c conda-forge mamba
   2. Next, create your Snakemake environment using Mamba by either:
      • Running the command $ mamba env create -f workflow/envs/snakemake.yaml from the main pgMAP_pipeline folder
      • Un-commenting line 5 in the script run_snakemake.sh (command: $ mamba env create -f workflow/envs/snakemake.yaml)

3. We suggest running the pgPEN tutorial, described below, in a separate folder before analyzing your own files.

4. Once you've successfully run the tutorial, copy your input FASTQ files into the folder input/fastqs/. Important note: Your FASTQ filenames must include either R1 and R2 (for the two-read sequencing approach) or R1, R2, and R3 (for the three-read sequencing approach). Additionally, all file extensions must include either .fastq or .fq.

5. Duplicate and update the files in the config/ folder as described below:

   • Make a copy of barcode_ref_file.sample.txt named barcode_ref_file.txt. Update the sample and barcode information to match your experimental design and sequencing setup. Note that the barcode and sample ID must be separated by a single tab.
   • Make a copy of config.sample.yaml named config.yaml. Update the base_filename variable, the input file path (the default is input/tutorial_fastqs), and the number of chunks to split your BAM files into. We suggest trying n_chunks: 50 for data from a full pgPEN CRISPR screen dataset.

6. Run the script run_snakemake.sh by entering the command: $ bash run_snakemake.sh. The following steps will run automatically for all samples specified in config/barcode_ref_file.txt:                   

More information on installing/running/troubleshooting Snakemake

Install Snakemake v7.1.0 using mambaforge as described here: https://snakemake.readthedocs.io/en/stable/tutorial/setup.html

Folder setup/running info as described here: https://snakemake.readthedocs.io/en/stable/snakefiles/deployment.html

pgMAP Tutorial

1. Downsampled pgPEN screen FASTQ files are used in the example, and can be found in the folder input/tutorial_fastqs/:

• PP_pgPEN_HeLa_S1_R1_001.fastq.gz
• PP_pgPEN_HeLa_S1_R2_001.fastq.gz
• PP_pgPEN_HeLa_S1_R3_001.fastq.gz

2. Make a copy of or rename config/barcode_ref_file.sample.txt as barcode_ref_file.txt, which is a file of sequencing barcodes:

CTTGTA   sample2
ACTTGA   sample1
GGCTAC   sample3

These barcodes are used in the demultiplexing step of the pipeline and are unique to each sample and condition. For the tutorial, you do not need to change the default values.

3. Make a copy of or rename config/config.sample.yaml as config.yaml. For the tutorial, you do not need to change the default values.

4. pgMAP is now ready to run. Execute run_snakemake_test.sh on an interactive compute node:

$ bash run_snakemake.sh

pgMAP will automatically install all required packages with dependencies. Snakemake will print output as the processes run and detail each step, including any errors that arise:                   

The resulting pgRNA counts can be found in results/pgRNA_counts/pgMAP_tutorial_pgRNA_counts.txt:

id	seq_1	seq_2	counts_sample1	counts_sample2	counts_sample3
AADAC_AADACL2_pg1	AAGTCTGAAGCACTAAGAAG	AAAGAAAGTCAGAAACCCGA	5	6	4
AADAC_AADACL2_pg10	ATTTCTATCCAAATCACTCA	GAAAAAATTTGACTGCAGCA	4	2	6
AADAC_AADACL2_pg11	ATTTCTATCCAAATCACTCA	GTGATGTATTCATCTGAAAG	0	2	3
AADAC_AADACL2_pg12	ATTTCTATCCAAATCACTCA	TGGGGGCAATTTAGCAACAG	1	2	1
AADAC_AADACL2_pg13	GGTATTTCTGGAGATAGTGC	AAAGAAAGTCAGAAACCCGA	1	2	2
AADAC_AADACL2_pg14	GGTATTTCTGGAGATAGTGC	GAAAAAATTTGACTGCAGCA	1	2	2

To confirm that the correct counts were generated, you can compare your output to the file results/expected_tutorial_output/expected_pgMAP_tutorial_pgRNA_counts.txt.

Snakemake will also generate reports in reports.html and workflow/report:

                                                      

All log (workflow/logs/) and intermediate files (results/) are also accessible to users.

Questions

Please contact Phoebe Parrish (developer) at pparrish@fredhutch.org, Daniel Groso (co-developer) at dgroso@fredhutch.org, or Alice Berger (Principal Investigator) at ahberger@fredhutch.org with any questions about pgMAP.

License

pgMAP is available open-source under the MIT license.

Cite pgMAP

Please cite pgMAP as arXiv:2306.00944