Custom analysis software for ChIP-seq of DNA repair proteins after CRISPR/Cas9-mediated DSBs

Citation

Please cite the following manuscript in press:

Liu, Y., Zou, R.S., He, S., Nihongaki, Y., Li, X., Razavi, S., Wu, B. and Ha, T., 2020. Very fast CRISPR on demand. Science, 368(6496), pp.1265-1269. https://doi.org/10.1126/science.aay8204

Software requirements

• python 2.7 (Anaconda's python distribution comes with the required numpy and scipy libraries)
• pysam
• bowtie2
• samtools
• Ensure that both samtools and bowtie2 are added to path and can be called directly from bash

Data requirements

• The data for MRE11 and γH2AX ChIP-seq before/after Cas9 activation with light can be downloaded from Here.
• The data from DISCOVER-seq (Wienert & Wyman et al, Science, 2019) for comparison can be downloaded from Here.

Generate processed BAM files (if starting from raw FASTQ files)

1. Download sequencing reads in FASTQ format from SRA
2. Download either the human or mouse prebuilt bowtie2 indices
   • Human hg38
   • Mouse mm10 (ftp://ftp.ccb.jhu.edu/pub/data/bowtie2_indexes/mm10.zip)
   • move to the corresponding folders named hg38_bowtie2/ or mm10_bowtie2/
3. Download either human (hg38) or mouse (mm10) genome assembly in FASTA format
   • hg38.fa
   • mm10.fa
   • extract from gz files
   • move to the corresponding folders named hg38_bowtie2/ or mm10_bowtie2/
4. Generate FASTA file indices
   • samtools faidx hg38_bowtie2/hg38.fa
   • samtools faidx mm10_bowtie2/mm10.fa
5. Modify the first lines of bash script process_reads.sh
   • Fill the list variable filelist with paths to each sample name for processing. Note that the actual file paths include the sample name followed by "_1.fastq" or "_2.fastq", denoting read1 or read2 of paired-end reads, respectively.
   • Fill in the path to the indexed genome denoted by variable genomepath.
6. Run the following code snippet, where -p denotes the number of samples to process in parallel; modify accordingly. This performs genome alignment, filtering, sorting, removal of PCR duplicates, indexing, and sample statistics output. This step takes less than one day to complete on our Intel i7-8700K, 32GB RAM desktop, though speed appears to be bottlenecked by read/writes to disk.

   bash process_reads.sh -p 6
   

7. (optional) For fair comparison between different time points in a timeseries, we subset reads from all relevant samples to the sample with the fewest reads of the set. Run the following code snippet, where -s inputs the number of mapped reads to subset for each sample; modify accordingly. This step takes less than 10 minutes.

   bash process_reads.sh -p 6 -s 24400000
   

   • for MRE11 timeseries, subsetted to 24,400,000 reads for both replicates.
   • for γH2AX timeseries, subsetted to 43,275,829 reads for replicate 1 and 62,271,079 reads for replicate 2.
   • for MRE11 DNA-PKcs inhibitor experiments, subsetted to 11,923,070 reads for both replicates.

Start from pre-processed BAM files

In addition to raw paired-end reads in FASTQ format, we have also uploaded pre-processed sequencing reads in BAM format to SRA. These are the output of the previous section. It is highly recommended to start from these BAM files.

1. Download pre-processed paired-end reads in BAM format from SRA.
2. Move the downloaded data for MRE11 and γH2AX ChIP-seq to the desired folder.
3. If not already done, index the downloaded BAM files:

   samtools index /path/to/output.bam
   

chipseq_mre11.py, chipseq_h2ax.py, chipseq_pki.py

chipseq_mre11.py: analyze MRE11 ChIP-seq data after activation of Cas9/cgRNA targeting ACTB

chipseq_h2ax.py: analyze γH2AX ChIP-seq data after activation of Cas9/cgRNA targeting ACTB

chipseq_pki.py: analyze MRE11 ChIP-seq data, with/without DNA-PKcs inhibitor KU60648

1. Set base variable to be the path to the directory that holds the BAM files.
2. Create a new folder to hold the output of the analysis, set its path to base_a.
3. Ensure that all file names are correct (if the BAM files were directly downloaded from SRA, they should be), then run script.

discoverseq_mre11.py

Analyze MRE11 ChIP-seq data from Wienert & Wyman et al (Science, 2019)

1. FASTQ reads with the following SRA run accession codes (SRR) were downloaded from Here.

   SRR8550692, SRR8550673, SRR8550703, SRR8550680, SRR8550681, SRR8550704, SRR8550684, SRR8550705, SRR8550693, SRR8550695, SRR8553800, SRR8553810, SRR8553804, SRR8553806
   

2. Generate BAM files from raw FASTQ reads following instructions from the previous sections. These files will be used in this script.
3. Ensure that the file names are correctly referenced in script, then run script.

discoverseq_others.py

Analyze ChIP-seq against multiple repair factors from Wienert & Wyman et al (Science, 2019)

1. FASTQ reads with the following SRA run accession codes (SRR) were downloaded from Here.

   SRR8550677, SRR8550696, SRR8550679, SRR8550694, SRR8550682, SRR8550699, SRR8550678, SRR8550697, SRR8550698, SRR8550690
   

2. Generate BAM files from raw FASTQ reads following instructions from the previous sections. These files will be used in this script.
3. Ensure that the file names are correctly referenced in script, then run script.