Custom analysis software for DISCOVER-Seq+

System requirements

• Anaconda Python 3.7 (Anaconda's python distribution comes with the required numpy and scipy libraries)
• pysam
• bowtie2
• samtools
• SRA Toolkit
  • For linux systems, download compiled binaries from here. In this example, will use Ubuntu Linux 64 bit architecture, downloading the file sratoolkit.3.0.0-ubuntu64.tar.gz.
  • Run tar -xvzf sratoolkit.3.0.0-ubuntu64.tar.gz to extract folder.
  • Copy extracted folder sratoolkit.3.0.0-ubuntu64 to permanent location, e.g. /home/roger/bioinformatics.
  • Add relevant functions to PATH variable by adding the following text to a new last line of ~/.bashrc, e.g. export PATH=$PATH:/home/roger/bioinformatics/sratoolkit.3.0.0-ubuntu64/bin.

Data requirements

• https://www.ncbi.nlm.nih.gov/bioproject/PRJNA801688

Installation guide (est. 30 min)

1. Download the prebuilt bowtie2 indices for various genome assemblies
   • Human hg38
   • Human hg19
   • Mouse mm10
   • Extract from archive, move to the corresponding folders named hg38_bowtie2/,hg19_bowtie2/, or mm10_bowtie2/
2. Download genome assemblies in FASTA format
   • hg38.fa
   • hg19.fa
   • mm10.fa
   • Extract from archive, move to the corresponding folders named hg38_bowtie2/,hg19_bowtie2/, or mm10_bowtie2/
3. Generate FASTA file indices
   • samtools faidx hg38_bowtie2/hg38.fa
   • samtools faidx hg19_bowtie2/hg19.fa
   • samtools faidx mm10_bowtie2/mm10.fa
4. Download BLENDER (Wienert et al., 2020)

Demo (please ensure 100 GB of free disk space)

This demo will perform DISCOVER-Seq+ on C57BL/6J mouse liver, 24h after induction with adenovirus expressing Cas9 and gRNA targeting PCSK9. Test run performed on personal Windows 10 Desktop with Ubuntu, Intel i7-8700k 3.7 GHz 6 cores, 32GB RAM.

1. On Linux/Mac command line, navigate to the home directory for this repository.
2. Open download_reads_demo.sh, go to "User Entry Section", enter the desired directory to download the sequencing data. For the consistency of this demo, I will use /mnt/c/Users/rzou4/Downloads/.
3. Run download_reads_demo.sh on command line, which will download the relevant FASTQ files from the public database, rename the files appropriately, and move files to newly created demo folder within /mnt/c/Users/rzou4/Downloads/ (est. time <10 min/file).
4. Open subset_reads_demo.sh, go to "User Entry Section", ensure the paths are correct.
5. Run subset_reads_demo.sh, which will subset the relevant FASTQ files to ensure equal # of sequencing reads between DISCOVER-Seq+ and DISCOVER-Seq, for fair comparison (est. time 1 min/file).
6. Open process_reads_demo.sh, go to "User Entry Section", enter the directory which has the mm10 genome previously installed via the Installation Guide.
7. Run process_reads_demo.sh, which will use bowtie2 to align the files to the mouse mm10 genome, followed by samtools for further processing before conversion to indexed BAM files (est. time 12 hours/file). Performs processing on all 3 samples in parallel.
8. Open blender_c3_demo.sh, go to "User Entry Section", enter the paths to (1) BLENDER software, (2) mm10 genome, and (3-4) path to new output folders.
9. Run blender_c3_demo.sh, which runs BLENDER to output list of off-target sites from DISCOVER-Seq+ versus DISCOVER-Seq (est. time 3 days/file). Performs processing on both samples in parallel.
10. Expected results are included in this repository:
    • DISCOVER-Seq+
    • DISCOVER-Seq

Usage instructions

Similar to instructions listed in the Demo, but more general and applicable to all data sets. The list of sequencing datasets is listed here.

download_reads.sh downloads the desired FASTQ sequencing reads from Sequencing Read Archive (SRA) (est. time 10 min/file), parallelizable

1. Open download_reads.sh, which has the list of FASTQ datasets to download, of format SRR********, such as SRR18188706, along with its corresponding name: mm_mP9_KU24h_r3. Enter the desired directory to hold the downloaded files at download_path. Uncomment only the files you would like to download. Expect the processing for each sample to take up ~33 GB of disk space.
2. Run download_reads.sh, which downloads the desired data from SRA using the SRA Toolkit, then renames and copies the downloaded files into a subfolder inside download_path called SRA_download

subset_reads.sh subsets the group of sequencing reads to ensure equal # of reads (est. time 1 min/file), parallelizable

3. Open subset_reads.sh, make sure sra_path points to the path for SRA_download folder. Under main() {}, uncomment only the files sets you would like to subset - likely they are the same ones previously downloaded in download_reads.sh. NOTE: negative control samples generally do not need to be subsetted b/c they are only used as a control for off-target detection using BLENDER.
4. Run subset_reads.sh to subset the relevant FASTQ files to ensure equal # of sequencing reads between DISCOVER-Seq+ and DISCOVER-Seq for fair comparison.

process_reads.sh aligns FASTQ to the appropriate genome, then converts alignment to BAM files (est. time 12 hours/file), parallelizable

5. Open process_reads.sh, uncomment only the files you would like to align and process - likely they are the same ones previously downloaded in download_reads.sh, some of which were subsetted with subset_reads.sh. Also, enter valid paths to the downloaded genomes
6. Run process_reads.sh to use bowtie2 to align the files to the mouse mm10 genome, followed by samtools for further processing before conversion to indexed BAM files.

blender_*_xxxx_cc.sh determines genome-wide off-target sites using BLENDER (est. time 1-3 days/file), parallelizable

7. Shell file of type blender_*_xxxx_cc.sh. * indicates sequencing file dataset, xxxx indicates genome (hg19 or mm10), and cc indicates either c2 or c3 BLENDER parameter.
8. Modify blender_*_xxxx_cc.sh appropriately for compatibility with your desired datasets, and run.

script_1.py perform downstream analysis, generating the majority of the data and results presented in the associated manuscript

9. Modify script_1.py appropriately for compatibility with your desired datasets, and run.