nf-core/crisprseq
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A pipeline for the analysis of CRISPR edited data. It allows the evaluation of the quality of gene editing experiments using targeted next generation sequencing (NGS) data (targeted) as well as the discovery of important genes from knock-out or activation CRISPR-Cas9 screens using CRISPR pooled DNA (screening).

crisprcrispr-analysiscrispr-casngs

These pages are for an old version of the pipeline (2.1.1). The latest stable release is 2.3.0 .

Launch version 2.1.1 https://github.com/nf-core/crisprseq

Introduction

The nf-core/crisprseq pipeline allows the analysis of CRISPR edited CRISPR pooled DNA. It can evaluate important genes from knock-out or activation CRISPR-Cas9 screens.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/crisprseq --analysis screening --input samplesheet.csv --library library.csv --outdir <OUTDIR> -profile docker

The following required parameters are here described.

Full samplesheet

The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 4 columns to match those defined in the table below.

sample,fastq_1,fastq_2,condition
SRR8983579,SRR8983579.small.fastq.gz,,control
SRR8983580,SRR8983580.small.fastq.gz,,treatment

Column	Description
`sample`	Custom sample name. Spaces in sample names are automatically converted to underscores (`_`).
`fastq_1`	Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”.
`fastq_2`	Full path to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”. (Optional)
`condition`	Condition of the sample, for instance “treatment” or “control”.

An example samplesheet has been provided with the pipeline.

library

If you are running the pipeline with fastq files and wish to obtain a count table, the library parameter is needed. The library table has three mandatory columns : id, target transcript (or gRNA sequence) and gene symbol. An example has been provided with the pipeline. Many libraries can be found on addgene.

After the alignment step, the pipeline currently supports 3 algorithms to detect gene essentiality, MAGeCK rra, MAGeCK mle and BAGEL2. MAGeCK MLE (Maximum Likelihood Estimation) and MAGeCK RRA (Robust Ranking Aggregation) are two different methods provided by the MAGeCK software package to analyze CRISPR-Cas9 screens. BAGEL2 identifies gene essentiality through Bayesian Analysis.

MAGeCK rra

MAGeCK RRA performs robust ranking aggregation to identify genes that are consistently ranked highly across multiple replicate screens. To run MAGeCK rra, --rra_contrasts contains two columns : treatment and reference. These two columns should be separated with a dot comma (;) and contain the csv extension. You can also integrate several samples/conditions by comma separating them. Please find an example here below :

reference	treatment
control1	treatment1
control1,control2	treatment1,treatment2

A full example can be found here.

cutadapt

MAGeCK is normally able to automatically determine the trimming length and sgRNA length, in most cases. Therefore, you don’t need to go to this step unless MAGeCK fails to do so by itself. If the nucleotide length in front of sgRNA varies between different reads, you can use cutadapt to remove the adapter sequences by using the flag --cutadapt ADAPTER.

MAGeCK mle

MAGeCK MLE uses a maximum likelihood estimation approach to estimate the effects of gene knockout on cell fitness. It models the read count data of guide RNAs targeting each gene and estimates the dropout probability for each gene. MAGeCK mle requires a design matrix. The design matrix is a txt file indicating the effects of different conditions on different samples. An example design matrix has been provided with the pipeline. If there are several designs to be run, you can input a folder containing all the design matrices. The output results will automatically take the name of the design matrix, so make sure you give a meaningful name to the file, for instance “Drug_vs_control.txt”.

Running CRISPRcleanR

CRISPRcleanR is used for gene count normalization and the removal of biases for genomic segments for which copy numbers are amplified. Currently, the pipeline only supports annotation libraries already present in the R package and which can be found here. To use CRISPRcleanR normalization, use --crisprcleanr library, library being the exact name as the library in the CRISPRcleanR documentation (e.g: “AVANA_Library”).

BAGEL2

BAGEL2 (Bayesian Analysis of Gene Essentiality with Location) is a computational tool developed by the Hart Lab at Harvard University. It is designed for analyzing large-scale genetic screens, particularly CRISPR-Cas9 screens, to identify genes that are essential for the survival or growth of cells under different conditions. BAGEL2 integrates information about the location of guide RNAs within a gene and leverages this information to improve the accuracy of gene essentiality predictions. BAGEL2 uses the same contrasts from --rra_contrasts.

Note that the pipeline will create the following files in your working directory:

work                # Directory containing the nextflow working files
<OUTDIR>            # Finished results in specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other hidden nextflow files, eg. history of pipeline runs and old logs.

nf-core/crisprseq Edit