offtracker 2.10.7

Tracking-seq data analysis

Offtracker

Offtracker is an end to end pipeline of Tracking-seq data analysis for detecting off-target sites of any genome editing tools that generate double-strand breaks (DSBs) or single-strand breaks (SSBs).

System requirements

• Linux/Unix
• Python >= 3.6

Dependency

# We recommend creating a new environment using mamba/conda to avoid compatibility problems
# If you don't use mamba, just replace the code with conda 
# Windows systems may not be compatible with pybedtools.
mamba create -n offtracker -c bioconda blast snakemake pybedtools deeptools chromap

Installation

# Activate the environment
conda activate offtracker

# Direct installation with pip
pip install offtracker

# (Alternative) Download the offtracker from github
git clone https://github.com/Lan-lab/offtracker.git 
cd offtracker
pip install .

Before analyzing samples

Important: Do not use hard-masked genome.fa, in which repeats are masked by capital Ns and reads should have been mapped to these region (e.g. MHC region) will be lost. Besides, the genome.fa should not contain alternate loci like chr2_KI270776v1_alt and chr6_GL000256v2_alt, which may cause multi-mappings and the reads may be discarded.

For example, https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz is soft-masked genome with alternate loci. https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.masked.gz is hard-masked genome. Do not use these two as reference genome.

http://cistrome.org/~galib/MAESTRO/references/scATAC/Refdata_scATAC_MAESTRO_GRCh38_1.1.0.tar.gz is the genome used for the example data.

# The following command can be used to check whether alternate loci of chr6 are present in the reference genome.
grep "^>chr6" genome.fa

# Build chromap index (only need once for each genome)
chromap -i -r /Your_Path_To_Reference/hg38_genome.fa \
-o /Your_Path_To_Reference/hg38_genome.chromap.index

# Build blast index (only need once for each genome)
makeblastdb -input_type fasta -title hg38 -dbtype nucl -parse_seqids \
-in /Your_Path_To_Reference/hg38_genome.fa \
-out /Your_Path_To_Reference/hg38_genome.blastdb \
-logfile /Your_Path_To_Reference/hg38_genome.blastdb.log

# Generate candidate regions by sgRNA sequence (need once for each genome and sgRNA)
# --name: a user-defined name of the sgRNA, which will be used in the following analysis.
offtracker_candidates.py -t 8 -g hg38 \
-r /Your_Path_To_Reference/hg38_genome.fa \
-b /Your_Path_To_Reference/hg38_genome.blastdb \
--name 'VEGFA2' --sgrna 'GACCCCCTCCACCCCGCCTC' --pam 'NGG' \
-o /Your_Path_To_Candidates_Folder

Quality control and adapter trimming

# Generate snakemake config file for quality control and adapter trimming.
offtracker_qc.py -t 4 \
-f /Your_Path_To_Input_Folder \
--subfolder 0

cd /Your_Path_To_Input_Folder/Trimmed_data
snakemake -np # dry run to check whether everything is alright
nohup snakemake --cores 16 1>${outdir}/sm_qc.log 2>&1 &

"""
Set “--subfolder 0” if the file structure is like: 
| - Input_Folder
  | - sample1_R1.fastq.gz
  | - sample1_R2.fastq.gz
  | - sample2_R1.fastq.gz
  | - sample2_R2.fastq.gz
Set “--subfolder 1” if the file structure is like:
| - Input_Folder
  | - Sample1_Folder
    | - sample1_R1.fastq.gz
    | - sample1_R2.fastq.gz
  | - Sample2_Folder
    | - sample2_R1.fastq.gz
    | - sample2_R2.fastq.gz

The script “offtracker_qc.py” will create a “Trimmed_data” folder under /Your_Path_To_Input_Folder. 
If “-o /Your_Path_To_Output” is set, the output will be redirected to /Your_Path_To_Output.
"""

Strand-specific mapping of Tracking-seq data

# Generate snakemake config file for mapping
# Results will be generated in /Your_Path_To_Output, if -o is not set, the output will be in the same folder as the fastq files
offtracker_config.py -t 8 -g hg38 --blacklist hg38 \
-r /Your_Path_To_Reference/hg38_genome.fa \
-i /Your_Path_To_Reference/hg38_genome.chromap.index \
-f /Your_Path_To_Trimmed_Data \
-o /Your_Path_To_Output \ 
--subfolder 0 

# Warning: Do not contain "fastq" or "fq" in the folder name, otherwise the program may treat the folder as a fastq file
# This problem may be fixed in the future

# Run the snakemake program
cd /Your_Path_To_Fastq
snakemake -np # dry run
nohup snakemake --cores 16 1>sm_mapping.log 2>sm_mapping.err &

## about cores
# --cores of snakemake must be larger than -t of offtracker_config.py
# parallel number = cores/t

## about output
# This part will generate "*.fw.scaled.bw" and ".rv.scaled.bw" for IGV visualization
# "*.fw.bed" and "*.rv.bed" are used in the next part.

Analyzing the genome-wide off-target sites

# In this part, multiple samples in the same condition can be analyzed in a single run by pattern recognition of sample names

offtracker_analysis.py -g hg38 --name "VEGFA2" \
--exp 'Cas9_VEGFA2' \
--control 'WT' \
--outname 'Cas9_VEGFA_293' \
-f /Your_Path_To_Output \
--seqfolder /Your_Path_To_Candidates

# --name: the same gRNA name you set when running offtracker_candidates.py
# --exp/--control: add one or multiple patterns of file name in regular expressions
# If multiple samples meet the pattern, their signals will be averaged. Thus, only samples with the same condition should be included in a single analysis.

# This step will generate Offtracker_result_{outname}.csv
# Default FDR is 0.05, which can be changed by --fdr. This will empirically make the threshold of Track score around 2.
# Sites with Track score >=2, which is a empirical threshold, are output regardless of FDR.
# Intermediate files are saved in ./temp folder, which can be deleted.
# Keeping the intermediate files can make the analysis faster if involving previously analyzed samples (e.g. using the same control samples for different analyses)

Off-target sequences visualization

# After get the Offtracker_result_{outname}.csv, you can visualize the off-target sites with their genomic sequence with the following command:

offtracker_plot.py --result Your_Offtracker_Result_CSV \
--sgrna 'GACCCCCTCCACCCCGCCTC' --pam 'NGG'

# The default output is a pdf file with Offtracker_result_{outname}.pdf
# Assigning a specific output file with another suffix can change the format. e.g., "--output Offtracker_plot.png" will generate a png file.
# The orange dash line indicates the empirical threshold of Track score = 2
# Empirically, the off-target sites with Track score < 2 are less likely to be real off-target sites.

Note1, when not using hg38 or mm10

The default setting only includes chr1-chr22, chrX, chrY, and chrM. (only suitable for human and mouse)
If you are using reference genomes without "chr" at the beginning, or want to analyze all chromosomes or other species, you can set "--ignore_chr" when running offtracker_config.py to skip chromosome filter.

Currently, this software is only ready-to-use for mm10 and hg38. For any other genome, e.g., hg19, please add a genome size file named "hg19.chrom.sizes" to .\offtracker\utility. Besides, add "--blacklist none" or "--blacklist Your_Blacklist" (e.g., ENCODE blacklist) when running offtracker_config.py, because we only include blacklists for mm10 and hg38.

Note2

The FDRs in the Tracking-seq result do not reflect the real off-target probability. It is strongly recommended to observe the "fw.scaled.bw" and "rv.scaled.bw" using genome browser like IGV to visually inspect each target location from the Tracking-seq result.

Example Data

Here are example data that contains reads of chr6 from HEK293T cells edited with Cas9 + sgRNA VEGFA_site_2 (VEGFA2) and reads of chr6 from wild type HEK293T cells:

https://figshare.com/articles/dataset/WT_HEK239T_chr6/25956034

It takes about 5-10 minutes to run the mapping (offtracker_config.py & snakemake) of example data with -t 8 and --cores 16 (2 parallel tasks)

Signal visualization

After mapping, there will be 4 .bw files in the output folder:

Cas9_VEGFA2_chr6.fw.scaled.bw

Cas9_VEGFA2_chr6.rv.scaled.bw

WT_chr6.fw.scaled.bw

WT_chr6.rv.scaled.bw

These files can be visualized in genome browser like IGV:

The signal (coverage) for each sample is normalized to 1e7/total_reads. As only reads mapping to chr6 were extracted in the example data, the signal range is much higher than that of the whole genome samples.

Whole genome off-target analysis

For analyzing the signals (offtracker_analysis.py), it takes about 3-5 minutes and outputs a file named "Offtracker_result_{outname}.csv"

After that, you can visualize the off-target sites with their genomic sequence (offtracker_plot.py) and get an image like this:

After finishing the pipeline, if “chr6:31400832-31400854” and “chr6:31495044-31495066” are missing in the plot, it is most likely due to either:

• Using a hard-masked reference genome (where repeats are replaced with 'N's)

• The presence of alternate loci (e.g., chr6_GL000256v2_alt) in the genome.

These two off-target sites locate in the region of MHC class I chain-related protein A and B (MICA and MICB), which is polymorphic (resulting in alternate loci in contigs like “chr6_GL000256v2_alt”) and contains interspersed repeats (resulting in sequences masked by capital 'N's in a hard-masked genome). Please try again with unmasked or soft-masked genome without alternate loci.

Citation

If you use Tracking-seq or OFF-TRACKER in your research, please cite the following paper:

Zhu, M., Xu, R., Yuan, J., Wang, J. et al. Tracking-seq reveals the heterogeneity of off-target effects in CRISPR–Cas9-mediated genome editing. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02307-y

The signal visualization of .bw file here was generated by the Integrative Genomics Viewer (IGV) software. The signal visualization in the Tracking-seq article above was generated by either IGV or pyGenomeTracks:

Robinson, J., Thorvaldsdóttir, H., Winckler, W. et al. Integrative genomics viewer. Nat Biotechnol 29, 24–26 (2011). https://doi.org/10.1038/nbt.1754

Lopez-Delisle L, Rabbani L, Wolff J, Bhardwaj V, Backofen R, Grüning B, Ramírez F, Manke T. pyGenomeTracks: reproducible plots for multivariate genomic data sets. Bioinformatics. 2020 Aug 3:btaa692. doi: 10.1093/bioinformatics/btaa692.