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CRISPR screen analysis

This pipeline performs the following tasks:

  • Create an isolated environment and intallation for CRISPR screen analysis
  • MAGeCK or MAGeCK-VISPR
  • Gene hit identification and downstream functional enrichment analysis(MAGeCKFlute)
  • [Optional]Adapter Trim, Batch effect removal,Correct copy-number bias.
  • Run by Bash file

System requirements

  • Linux/Unix
  • Python
  • R

Wiki

https://sourceforge.net/p/mageck/wiki/Home/ https://www.bioconductor.org/packages/devel/bioc/vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute.html#install-and-load-the-required-packages

Installation

We uses the Miniconda3 package management system to harmonize all of the software packages. Use the following commands to install Minicoda3:

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

Create an isolated environment for CRISPR screen analysis

conda create -n CRSIPR
conda activate CRSIPR

Install tools

Tools needed for this analysis are: R, MAGeCK, libxml2, MAGeCK-VISPR,MAGeCKFlute

conda config --add channels conda-forge
conda config --add channels bioconda
conda config --add channels r
conda install -c anaconda libxml2
conda install -c bioconda mageck
conda install -c bioconda -c conda-forge mageck-vispr

Install MAGeCKFlute using R

R

> install.packages(c("devtools", "BiocManager"), repos = "https://cloud.r-project.org")
> BiocManager::install(c("pathview", "biomaRt", "msigdbr", "dendextend", "pheatmap", "sva", "ggrepel", "knitr", "clusterProfiler", "depmap"))
> BiocManager::install("MAGeCKFlute") # Released version
# Or
> devtools::install_github("liulab-dfci/MAGeCKFlute")

Processing of CRISPR screen data with MAGeCK or MAGeCK-VISPR

In a typical use case, CRISPR screen data are processed with MAGeCK (option A) step by step. If users want to perform QC and visualize the results, we recommend MAGeCK-VISPR (option B) instead.

A.Process CRISPR screen data step by step with MAGeCK ● Timing 1.5 h

#Activate the CRSIPR environment 
conda activate CRSIPR

# Download and unzip the test data for both datasets, using the following commands:
wget http://cistrome.org/MAGeCKFlute/demo.tar.gz 
tar zxvf demo.tar.gz
cd demo_data

# Generate a count table for Dataset 1 with the mageck count function, by first changing the working directory to a directory that contains raw .fastq data and is able to store the output of mageck count as follows:
cd path/to/demo_data/mageck_count

# To run the mageck count on Dataset 1, type the following command:
mageck count -l library.csv -n GSC_0131 --sample-label day0_r1, day0_r2,day23_r1,day23_r2 --fastq GSC_0131_Day0_Rep1.fastq.gz GSC_0131_Day0_Rep2.fastq.gz GSC_0131_Day23_Rep1.fastq.gz GSC_0131_ Day23_Rep2.fastq.gz

# Identify screen hits using MAGeCK RRA(MAGeCK RRA for comparison between two conditions, such as an initial condition versus cells cultured for a period of time. )
$mageck test -k GSC_0131.count.txt -t day23_r1,day23_r2 -c day0_r1,day0_r2 -n GSC_0131_rra --remove-zero both --remove- zero-threshold 0

# Identify screen hits using MAGeCK MLE. (If an experiment contains more than two conditions, for example, a three-condition design: day 0, drug treatment and DMSO treatment, we recommend using MAGeCK MLE)
cd path/to/demo_data/mageck_mle
mageck mle --count-table rawcount.txt --design-matrix designmatrix. txt --norm-method control --control-sgrna nonessential_ctrl_sgrna_ list.txt --output-prefix braf.mle ##modify designmatrix table as your expriemnt design

(B) Process CRISPR screen data with MAGeCK-VISPR ● Timing 1.5 h

#Activate the CRSIPR environment 
conda activate CRSIPR

#Choose a workflow directory and initialize the workflow with the .fastq or .fastq.gz files that contain the raw reads
mageck-vispr init workflow --reads path/to/file/*.fastq*

#Configure the workflow
cd workflow

# To check whether the ‘config.yaml’ files have been configured correctly,enter the following command line into the terminal:
$snakemake –n

# Execute the workflow.
snakemake --cores 8

# Visualize the results with VISPR.
vispr server results/*.vispr.yaml

MAGeCKFlute

This package implements methods to perform quality control (QC), normalization, batch effect removal, gene hit identification and downstream functional enrichment analysis for CRISPR screens.

Functional analysis for MAGeCK RRA results

#Activate the CRSIPR environment 
conda activate CRSIPR

#use the R interactive shell
R

 >library(MAGeCKFlute)
 >setwd(‘path/to/file/’)
 
# To perform functional analysis for MAGeCK RRA results 
>FluteRRA(gene_summary = "path/to/file/rra.gene_summary.txt", prefix="FluteRRA", organism="hsa")
 

Functional analysis for MAGeCK RRA results

#Activate the CRSIPR environment 
conda activate CRSIPR

#use the R interactive shell
R

 >library(MAGeCKFlute)
 >setwd(‘path/to/file/’)
 
# To perform functional analysis for MAGeCK MLE  results 
>FluteMLE(gene_summary="path/to/file/mle.gene_summary.txt", ctrlname="dmso", treatname="plx", organism="hsa", prefix="FluteMLE", -pathway_limit = c(3,50))


(Optional) Batch effect removal

R
> library(MAGeCKFlute)
> BatchRemove(mat = "rawcount.txt", batchMat = "BatchMatrix. txt", prefix = "BatchCorrect", -pca = T, -cluster = T, -outdir = ".")

(Optional) Correct copy-number bias.

MAGeCK RRA and MAGeCK MLE contain an optional method to correct copy-number biases in the calculated RRA scores and beta scores, respectively. We recommend that users perform copy-number bias correction if the CNV information is available for the cell line.

# To perform MAGeCK RRA with copy-number bias correction, type the following:
mageck test -k rawcount.txt -t HL60.final -c HL60.initial -n rra_ cnv --cnv-norm cnv_data.txt –cell-line HL60_HAEMATOPOIETIC_AND_ LYMPHOID_TISSUE

# To perform MAGeCK MLE with copy-number bias correction, type the following:
mageck mle --count-table rawcount.txt --design-matrix designma- trix.txt --cnv-norm cnv_data.txt

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