Introduction

nf-core/crispriscreen is a bioinformatics best-practice analysis pipeline to process next generation sequencing data obtained from CRISPRi repression library screenings.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

Pipeline summary

1. Sub-sampling of reads (Seqtk/sample, optional)
2. Read QC (FastQC)
3. Generic adapter and quality trimming (Trim Galore!)
4. Specific primer sequence trimming (cutadapt)
5. Preparation of *.fasta library (custom R script)
6. Alignment using (Bowtie2)
   1. Build index from *.fasta library
   2. Align reads to library
   3. Optional filtering by mapping quality
7. Count reads per target and input file (subread/featurecounts)
8. Quantify gene fitness score from multiple targets per gene
   1. Option 1: Gene fitness is calculated using Mageck MLE
   2. Option 2: Gene fitness is calculated using DESeq2
9. Generate HTML report with fitness results (R markdown)
10. Present QC for raw and mapped reads (MultiQC)

Quick Start

1. Install Nextflow (>=23.04.0). It is recommended to use conda (or mamba / micromamba) to install all dependencies in a fresh environment.

   conda create --name env_nf
   conda activate env_nf
   conda install -c conda-forge nextflow

2. Install any of Docker, Singularity (you can follow this tutorial), Podman, Shifter or Charliecloud for full pipeline reproducibility (you can use Conda both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs).

   conda install -c conda-forge singularity

3. Download the pipeline.

   cd <your/target/dir>
   git clone https://github.com/MPUSP/nf-core-crispriscreen

4. Configure your computational resources. The pipeline comes with a set of basic resource definitions stored in conf/base.config. Maximum resources in terms of memory, CPU cores, and time can be defined for each process flag separately, for example:

   withLabel:process_low {
       cpus   = { 2     * task.attempt }
       memory = { 12.GB * task.attempt }
       time   = { 4.h   * task.attempt }
   }

   In order to use custom resource configurations, create a new file custom.config and execute the pipeline with the -c flag.

   nextflow run <path/to/nf-core-crispriscreen> -c custom.config <all/other/options>

5. Test it on the minimal dataset included with this repository. Since nf-core-crispriscreen is not a canonical nf-core pipeline (yet), it is necessary to indicate the path to the pipeline folder after the run statement.

   This is the generalized command to run the pipeline:

   nextflow run <path/to/nf-core-crispriscreen> -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input <sample_sheet> --fasta <fasta_file> --outdir <path/to/output>

   This will run the pipeline on the enclosed test data using Singularity (recommended):

   cd path/to/nf-core-crispriscreen
   nextflow run ./ -profile test,singularity --outdir ./results

   • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
   • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
   • If you are using singularity, please use the nf-core download command to download images first, before running the pipeline. Setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you to store and re-use the images from a central location for future pipeline runs.
   • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.

Documentation

The nf-core/crispriscreen pipeline comes with documentation about the pipeline usage and output.

Credits

The following people contributed to the nf-core/crispriscreen pipeline:

• Dr. Michael Jahn, MPUSP Berlin (ORCID) - main author and maintainer
• Dr. Ute Hoffmann, Science for Life Lab, Stockholm - contributor

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on the Slack #crispriscreen channel (you can join with this invite).

Citations

The following scientific articles are related to this pipeline.

The new version of CRISPRi libary for which this Nextflow pipeline was developed:

Rui Miao, Michael Jahn, Kiyan Shabestary, Elton Paul Hudson. CRISPR interference screens reveal tradeoffs between growth rate and robustness in Synechocystis sp. PCC 6803 across trophic conditions bioRxiv 2023.02.13.528328. https://doi.org/10.1101/2023.02.13.528328

The original publication of the CRISPRi library for which a precursor of this pipeline was developed:

Lun Yao, Kiyan Shabestary, Sarah Björk, Johannes Asplund-Samuelsson, Hakan Joensson, Michael Jahn & Elton Paul Hudson. Pooled CRISPRi screening of the cyanobacterium Synechocystis sp PCC 6803 for enhanced industrial phenotypes. Nature Communications, 11 (1666), 1–13. 2020. https://doi.org/10.1101/823534

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.