bm2-lab / igwos

iGWOS: integrated Genome-Wide Off-target cleavage Search

iGWOS

integrated Genome-Wide Off-target cleavage Search platform

Introduction

iGWOS is designed for the optimal genome-wide off-target sites (OTS) prediction by integrating the available OTS prediction tools in a complementary way. iGWOS integrates OTS prediction tools with an Adaboost framework, supports conventional NGG-PAM OTS prediction with mismatches up to 6 in human species.

iGWOS searches the candidate off-target sites with Cas-OFFinder and predict CRISPR/Cas9-induced off-target cleavage sites by integrating distinct OTS prediction tools (CRISPRoff, uCRISPR, DeepCRISPR, CFD, MIT, CROP-IT and CCTop). The genome encode way can be referred to DeepCRISPR (https://github.com/bm2-lab/DeepCRISPR)

By inputting the gRNA(s) sequence file and related restrictions, iGWOS precisely predicts the genome-wide OTS list of given gRNAs, and visualizes the top 200 risky genome-wide off-target profile with a Circos plot. The iGWOS score of a off-target site denotes its cleavage probability.

Requirement

• python==3.7
• pandas==0.20.1
• numpy==1.14.5
• pyfaidx==0.4.8.4
• tensorflow==1.8.0
• sonnet==1.33
• uCRISPR==0.1
• RNAstructure==6.2
• python==2.7
• biopython==1.73
• ViennaRNA==2.4.12
• RIsearch==2.1
• circos==0.69-6

Install

1. git clone https://github.com/bm2-lab/iGWOS.git

2. Install the dependency:

    # pull the image from Docker Hub
    $ docker pull jafferyan/igwos_1.0:latest 
   

Usage

python3 main.py [-h] [-v] [-gRNA GRNA] [-g GENOME] [-m {0,1,2,3,4,5,6}]
         [-cell CELL] [-cid CID] [-e ENCODE] [-circos {0,1}] [-gpu GPU]
         [-o OUTPUT]

Predict genome-wide CRISPR-Cas9 off-target sites with iGWOS.

optional arguments:
-h, --help        show this help message and exit
-v, --version     show program's version number and exit
-gRNA GRNA        gRNAs file in Fasta format
-g GENOME         genome folder for candidate off-target searching,
                    default=genome/hg19
-m {0,1,2,3,4,5,6}  maximum mismatch allowed in off-target prediction,
                    default=5
-cell CELL        cell-type of gRNAs
-cid CID          cell-id file, formed like data/encode_hg19.tab
-e ENCODE         epigenomic encode folder, default=/data/genome/encode/fa/
-circos {0,1}     whether to draw a circos plot to visualize the top 200
                  risky predicted off-target profile, default=1
-gpu GPU          select a gpu device to perform cas-offinder and/or
                    deepcrispr, default=0
-o OUTPUT         output folder, default=output/

Example

python3 main.py -gRNA data/grna.fa -g genome/hg19 -m 5 -cell K562 -cid data/encode_hg19.tab -e /data/genome/encode/fa/ -circos 1 -gpu 1 -o output

gRNA file format

>sg1
GCCTCCCCAAAGCCTGGCCAGGG
>sg2
GGCCAGGCTTTGGGGAGGCCTGG

cell-id file format: [cid] [cell]

h1	MCF-7
h2	GM12878
h3	HepG2
h4	LNCaP clone FGC
h5	HCT116
h6	HeLa-S3
h7	K562

encode folder format (take K562 cell as an example)

Format: [encode_path]/[cid]_[epi].fa

/data/genome/encode/fa/h7_ctcf.fa
/data/genome/encode/fa/h7_dnase.fa
/data/genome/encode/fa/h7_h3k4me3.fa
/data/genome/encode/fa/h7_rrbs.fa

output format

sgID	gRNA	OTS	Chr	Strand	Start	Mismatch	iGWOS
sg1	GCCTCCCCAAAGCCTGGCCAGGG	GCCTCCCCAAAAGCTGAGCAGGG	chr1	+	929401	4	0.3254589692131059
sg1	GCCTCCCCAAAGCCTGGCCAGGG	TGCTCCCCAGAGCCTAGCCGTGG	chr7	-	1900836	5	0.46115958387343875
sg1	GCCTCCCCAAAGCCTGGCCAGGG	ACCTCCCCATAGCCTGGCCAGGG	chr11	-	44986455	2	0.529510001952807

OTS Circos visualization format

Citation

Yan, J., Liu, Q., et al. (2020). "Benchmarking and integrating genome-wide CRISPR off-target detection and prediction." Nucleic Acids Res 48(20): 11370-11379.

Contacts

[email protected] or [email protected]

igwos's Issues

Error in launching docker image

Hi, I was using windows WSL to run the docker image, but failed to launch it.

Could you provide a quick tutorial for that?
Thanks!

Dockefile question

Hello,

The Dockerfile states the following source
FROM gconda:latest
but I cannot find such an image in docker.com. Is this on another repository?

# data missing

Hi, I can't find the example data of epigenomic encode, as it supposed to be 'default=/data/genome/encode/fa/'.
Thanks a lot!

Dependencies question

Hello,

In the dependencies, both Python 2.7 and 3.7 are listed. Are two python environments are required?

data missing

Hi, I can't find the example data of epigenomic encode, as it supposed to be 'default=/data/genome/encode/fa/'.
Thanks a lot!