CRISPR Pipeline - README

Overview

All the steps are perform in linux.
This repository provides a step-by-step guide for performing quality control, trimming, alignment, variant calling, and annotation of CRISPR data. The pipeline includes downloading raw sequencing data, processing it, and identifying genetic variants.

Prerequisites

Ensure that the following tools are installed on your system:

• wget: Used to download raw sequencing data and reference genomes.
• gunzip: For decompressing .gz files.
• FastQC: For quality control of raw reads.
• fastp: For trimming sequencing reads.
• BWA: For aligning the sequencing reads to a reference genome.
• samtools: For sorting, removing duplicates, and converting file formats.
• GATK: For variant calling.
• picard-tools: Required for file format conversion and preparing BAM files for GATK.
• SnpEff: For variant annotation.
• VEP: Used for annotating variants based on the genome.

Pipeline Steps

Step 1: Downloading Raw Sequencing Data

Use wget to download raw FASTQ files from the SRA repository:

wget -c ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR217/020/SRR21763320/SRR21763320_1.fastq.gz
wget -c ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR217/020/SRR21763320/SRR21763320_2.fastq.gz

Step 2: Data Extraction

Decompress the FASTQ files:

gunzip *.gz

Step 3: Quality Control

Run quality control using FastQC:

fastqc *.fastq

Check the following parameters:

1. Per base sequence quality
2. Overrepresented sequences
3. Adapter content

Step 4: Trimming

Trim the reads using fastp:

Create a adapter file first

touch adapter.fasta

These are the Universal Adapters: Illumina Universal Adapter = AGATCGGAAGAG
Illumina Small RNA 3' Adapter = TGGAATTCTCGG
Illumina Small RNA 5' Adapter = GATCGTCGGACT
Nextera Transposase Sequence = CTGTCTCTTATA
PolyA = AAAAAAAAAAAA
PolyG = GGGGGGGGGGGG

Open newly created adapter.fasta file in Notepad and write:

>H1
AGATCGGAAGAG

after this perform below command.

fastp -i sample.fastq -o trim_sample.fastq --adapter_fasta adapter.fasta

Run quality control on the trimmed reads:

fastqc trim_sample.fastq

Step 5: Aligning Reads to the Reference Genome

Download the reference genome:

wget -c https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr17.fa.gz
gunzip chr17.fa.gz
mv chr17.fa genome.fa

Index the reference genome:

bwa index -a bwtsw genome.fa

Align the reads to the reference genome:

bwa mem -t 2 genome.fa SRR21763320_1.fastq SRR21763320_2.fastq > bwa_SRR21763320.bam

Step 6: Sorting and Conversion

Sort the BAM file using samtools:

samtools sort bwa_SRR21763320.bam > sorted_SRR21763320.bam

Convert the BAM file to SAM:

samtools view sorted_SRR21763320.bam > sorted_SRR21763320.sam

Step 7: Removing Duplicates

Remove duplicate reads using samtools:

samtools rmdup -sS sorted_SRR21763320.bam rmdup_SRR21763320.bam

Step 8: Variant Calling

Download and install GATK:

wget -c https://github.com/broadinstitute/gatk/releases/download/4.3.0.0/gatk-4.3.0.0.zip

Convert the reference genome to Picard-tools format:

picard-tools CreateSequenceDictionary R=genome.fa O=genome.dict

Prepare the BAM file for GATK:

picard-tools AddOrReplaceReadGroups I=rmdup_SRR21763320.bam O=picard_output.bam RGLB=lib1 RGPL=illumina RGPU=run RGSM=SRR21763320 SORT_ORDER=coordinate CREATE_INDEX=true VALIDATION_STRINGENCY=LENIENT

Call variants with GATK:

samtools faidx genome.fa
java -jar /path/to/gatk-package-4.3.0.0-local.jar HaplotypeCaller -R genome.fa -I picard_output.bam -O GATK_output.vcf

Step 9: Variant Filtering

Filter variants using SnpSift:

cat GATK_output.vcf | java -jar /path/to/SnpSift.jar filter "(( QUAL>=30) & (DP>=10) & (MQ>=30))" > filter.vcf

Step 10: Variant Annotation

Annotate variants using VEP: Refer to the VEP documentation for more details.

java -jar snpEff.jar chr3 GATK_output.vcf > VEP_output.vcf

License

This project is licensed under the MIT License.

Acknowledgments

This pipeline was inspired by various bioinformatics tools and resources such as FastQC, BWA, GATK, and VEP.