nf-core pipeline · crispr, crispr-analysis, crispr-cas
nf-core/crisprseq
A Nextflow pipeline for evaluating targeted gene editing quality and identifying hits from pooled CRISPR screens.
What nf-core/crisprseq is
nf-core/crisprseq is a bioinformatics pipeline designed to process next-generation sequencing data from CRISPR gene editing experiments. It caters to two main analytical workflows: targeted sequencing to quantify editing outcomes, and pooled CRISPR-Cas9 screens to detect significant genes linked to knockout, activation, or interference phenotypes.
The targeted analysis component is based on CRISPR-Analytics (CRISPR-A), originally developed at Universitat Pompeu Fabra, and was rewritten in Nextflow DSL2 alongside the screening workflow by researchers at the Quantitative Biology Center (QBiC) at Universität Tübingen. The tool resolves common challenges in CRISPR data handling by consolidating quality control, alignment, error correction, and statistical ranking into a standardised workflow.
By supporting diverse modalities—such as base editing, prime editing, knock-ins, knockouts, and pooled screens—the pipeline provides researchers with reproducible, containerised workflows for quantifying editing efficiencies and discovering functional genetic elements.
What the pipeline does
For targeted gene editing data, the pipeline begins by merging paired-end sequencing reads with Pear, followed by quality control with FastQC, adapter removal via Cutadapt, and sequence filtering with Seqtk. When unique molecular identifiers (UMIs) are utilised, the pipeline extracts and clusters them with Vsearch, generates cluster consensus sequences with minimap2 and racon, and performs polishing with Medaka. Processed reads are aligned to the target locus using minimap2 by default (or alternatively BWA or Bowtie 2), and editing outcomes are identified through CIGAR string parsing implemented in R.
For pooled screening data, the workflow performs FastQC before quantifying guide abundance using MAGeCK count (default) or Bowtie 2. Users can apply copy number variation (CNV) correction and normalisation using CRISPRcleanR. Single guide RNAs (sgRNAs) and target genes are then evaluated and ranked using MAGeCK test, MAGeCK MLE, BAGEL2, or DrugZ, with optional hit selection to prioritise probable true positives for knockout screens before generating final visualisations.
Key facts
- Workflow framework
- Nextflow DSL2
- Input formats
- FASTQ files (paired-end or single-end) defined in a CSV samplesheet
- Targeted alignment engines
- minimap2 (default), BWA, Bowtie 2
- Screening analysis tools
- MAGeCK, CRISPRcleanR, BAGEL2, DrugZ
- Main developers
- Laurence Kuhlburger and Júlia Mir Pedrol (QBiC, Universität Tübingen)
- Latest release
- 2.3.0 (2024-11-08)
What you can use it for
- Targeted Gene Knockout and Knock-in Assessment
- Quantify on-target insertion, deletion, and knock-in efficiency from targeted amplicon sequencing experiments.
- Base and Prime Editing Outcome Quantification
- Evaluate precise nucleotide substitutions and engineered edits generated by base editors or prime editors via CIGAR string parsing.
- Pooled CRISPR Knockout Screens
- Count sgRNA abundance across experimental conditions and identify essential genes using MAGeCK, BAGEL2, or DrugZ.
- CRISPR Activation and Interference Screens
- Analyse pooled CRISPRa or CRISPRi data to detect genes whose transcriptional upregulation or repression confers a phenotype.
Run it here
The research agent runs nf-core/crisprseq with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
Strengths and limits
Good at
- Supports both targeted sequencing evaluations and genome-wide pooled screening datasets within a single pipeline architecture.
- Offers integrated UMI clustering, consensus generation, and Medaka polishing for low-frequency variant and targeted edit detection.
- Incorporates multiple specialised screening tools, including MAGeCK, BAGEL2, DrugZ, and CRISPRcleanR for copy number variation correction.
- Uses single-process containerisation via Nextflow DSL2 to ensure cross-platform reproducibility and simplified dependency management.
Watch out for
- When using Apptainer or Singularity with Nextflow version 23.07.0 or newer, users must configure the host home directory mount to allow MAGeCKFlute write access.
- Pipeline execution configuration must be passed via the command line or parameter files, as custom Nextflow configuration files cannot be used to declare pipeline parameters.
Questions
Can I run nf-core/crisprseq without my own cluster?
The research agent runs nf-core/crisprseq with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
What licence is nf-core/crisprseq under?
MIT, like every nf-core pipeline.
What editing modalities can be analysed in targeted mode?
The targeted workflow supports CRISPR gene knockouts, knock-ins, base editing, and prime editing experiments.
What screening platforms and libraries are compatible?
The screening mode accommodates pooled CRISPR-Cas9 screens, including knockout (KO), CRISPR activation (CRISPRa), and CRISPR interference (CRISPRi) assays.
Can the pipeline handle unique molecular identifiers (UMIs)?
Yes. For targeted sequencing, the pipeline provides an optional UMI workflow involving clustering with Vsearch, consensus building with minimap2 and racon, and polishing with Medaka.
How are input files provided to nf-core/crisprseq?
Inputs are supplied using a CSV samplesheet specifying sample names, FASTQ file paths, and experimental parameters such as reference sequences, protospacers, or condition labels depending on whether the targeted or screening mode is chosen.
Why does running MAGeCKFlute fail on newer versions of Nextflow with Singularity?
Nextflow version 23.07.0 and later stopped automatically mounting the host home directory. Because MAGeCKFlute requires write access to the home directory, users must set the environment variable NXF_SINGULARITY_HOME_MOUNT (or NXF_APPTAINER_HOME_MOUNT) to true before launching the pipeline.
How to cite it
Sanvicente-García M, García-Valiente A, Jouide S, Jaraba-Wallace J, Bautista E, Escobosa M, et al. (2023) CRISPR-Analytics (CRISPR-A): A platform for precise analytics and simulations for gene editing. PLoS Comput Biol 19(5): e1011137. https://doi.org/10.1371/journal.pcbi.1011137
Written from nf-core/crisprseq's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.