CRISPR-detector

CRISPR-detector provides a web-hosted platform (https://db.cngb.org/crispr-detector/) and local deployable pipeline to fast and accurately identify and annotate editing-induced mutations from genome editing assays.

CRISPR-detector pipeline possesses 4 key innovations :

optimized processing speed allowing for hundreds of amplicons and whole genome scanning;
edited and control sample co-analysis, to remove background variants exist prior to genome editing;
functional and clinical annotation of editing-induced mutations;
integrated structural variation (SV) calling.

System requirements

Sentieon module

Download sentieon toolkit from https://s3.amazonaws.com/sentieon-release/software/sentieon-genomics-202010.03.tar.gz
You may request a license by sending emails to huanglei@genomics.cn

export SENTIEON_LICENSE=PATH_TO_SENTIEON/sentieon-genomics-202010.03/localhost_eval.lic
export PATH=PATH_TO_SENTIEON/sentieon-genomics-202010.03/bin:$PATH

Python packages

pip install biopython
pip install pyfaidx
pip install -U textwrap3
conda install blast
conda install samtools

ANNOVAR

Download ANNOVAR from https://www.openbioinformatics.org/annovar/annovar_download_form.php

perl annotate_variation.pl -downdb -webfrom annovar avdblist humandb/ -buildver hg38
perl annotate_variation.pl -buildver hg38 -downdb -webfrom annovar refGene humandb/
perl annotate_variation.pl -buildver hg38 -downdb -webfrom annovar clinvar_20210501 humandb/
export PATH=PATH_TO_ANNOVAR/annovar:$PATH

Organism Homo sapiens experiment type sequencing data support variant annotations from refGene & ClinVar, other species may only support refGene annotations

You may build ANNOVAR database youself for any species with coresponding genome assembly and gff3 format files

For example, to build a database for zebrafish. Download GRCz11.fa and GRCz11.gff3 from public database.
Then running commands as following:

conda install -c bioconda/label/cf201901 gffread
conda install -c bioconda/label/cf201901 ucsc-gtftogenepred
conda install -c bioconda/label/cf201901 blast

cd PATH_TO_ANNOVAR/ mkdir zebrafishdb && cd zebrafishdb
mv */GRCz11.fa zebrafishdb
mv */GRCz11.gff3 zebrafishdb

gffread GRCz11.gff3 -T -o GRCz11.gtf
gtfToGenePred -genePredExt GRCz11.gtf GRCz11_refGene.txt
retrieve_seq_from_fasta.pl --format refGene --seqfile GRCz11.fa GRCz11_refGene.txt --out GRCz11_refGeneMrna.fa
makeblastdb -in GRCz11.fa -dbtype nucl

Usage

1. Single amplicon & pooled amplicons sequencing data analysis

python CRISPRdetectorCORE.py

--sample: sample name & output dir

--e1: treatment group fq1 path, required = True

--e2: treatment group fq2 path, required = False

--c1: control group fq2 path, required = False

--c2: control group fq2 path, required = False

--sample: sample name & output dir name, required = True

--amplicons_file: a tab-delimited text amplicons description file with up to 3 columns, required=True
[AMPLICON_NAME, AMPLICON_SEQ, gRNA_SEQ_without_PAM(Optional)]

--threads: number of threads to run sentieon minimap2 & driver module, default=1

--anno: annotate variants with ANNOVAR [1] or not run ANNOVAR [2], required = False

--assembly: assembly version, hg19,hg38 ...

--db: ANNOVAR database path, required = False

--ClinVar: only organism homo sapiens experiment type sequencing data support variant annotations from ClinVar[1], default = 0

--cleavage_offset: Center of quantification window to use within respect to the 3-end of the provided sgRNA sequence, default = -3

--window_size: defines the size (in bp) of the quantification window extending from the position specified by the cleavage_offset parameter in relation to the provided guide RNA sequence, 0 means whole amplicon analysis, default = 0

--o: output path, default='.', required = False

--ignore_substitutions: enable substitutions evaluation[1], default = 0

--min_tumor_allele_frac: the minimum allelic fraction in treated sample, default = 0.005

--min_num_of_reads: the minimum number of reads (per locus site) to evaluate, default = 500

--max_fisher_pv_active: the maximum pvalue of the statistical difference between treated and untreated sample, default = 0.05

2. Whole genome sequencing (WGS) data analysis

python CRISPRdetectorWGS.py