GSE55887 Processing Pipeline

Publication

Nature communications (2022) — PMID 35236841

Dataset

Genome-wide analysis reveals characteristics of off-target sites bound by the Cas9 endonuclease

1. 1

   Illlumina MiSeq Sequencer was used for base calling.

   MiSeq Reporter (Inferred with models/gemini-2.5-flash) vNot specified (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Note: Base calling itself is performed by the Illumina MiSeq's internal Real-Time Analysis (RTA) software, which is part of the MiSeq Reporter suite.
   # The following command demonstrates the typical next step in a bioinformatics pipeline: converting the raw BCL files (output of base calling) into FASTQ files using bcl2fastq.
   
   # Installation (example using conda):
   # conda install -c bioconda bcl2fastq2
   
   # Example command for bcl2fastq:
   # Replace /path/to/MiSeq/run/folder with the actual path to your MiSeq run directory containing BCL files.
   # Replace /path/to/output/fastq with your desired output directory for FASTQ files.
   bcl2fastq --runfolder-dir /path/to/MiSeq/run/folder \
             --output-dir /path/to/output/fastq \
             --no-lane-splitting \
             --minimum-trimmed-read-length 8 \
             --mask-short-adapter-reads 8 \
             --ignore-missing-bcl \
             --ignore-missing-stats \
             --ignore-missing-filter

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2. 2

   Sequence reads were aligned to hg19 reference genome using Bowtie for HA-Chip data and Bowtie2 for deep sequencing data.

   Bowtie v1.x (for Bowtie), 2.x (for Bowtie2) (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install Bowtie and Bowtie2
   # conda install -c bioconda bowtie bowtie2
   
   # Create a directory for reference genome and indices
   mkdir -p reference_hg19
   cd reference_hg19
   
   # Download hg19 reference genome (UCSC build)
   # wget https://hgdownload.soe.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz
   # gunzip hg19.fa.gz
   
   # For demonstration, let's assume hg19.fa is already present in reference_hg19/
   # If you downloaded it, uncomment the lines above and ensure the file is named hg19.fa
   
   # Build Bowtie index for hg19
   # bowtie-build hg19.fa hg19_index_bowtie
   
   # Build Bowtie2 index for hg19
   # bowtie2-build hg19.fa hg19_index_bowtie2
   
   cd ..
   
   # --- Example for HA-Chip data alignment with Bowtie ---
   # Assume input_ha_chip.fastq is your HA-Chip sequencing data (single-end)
   # For demonstration, create a dummy fastq file:
   # echo "@read1\nAGCTAGCTAGCTAGCT\n+\nIIIIIIIIIIIIIIII" > input_ha_chip.fastq
   
   bowtie -S -p 8 reference_hg19/hg19_index_bowtie input_ha_chip.fastq > output_ha_chip.sam
   
   # --- Example for deep sequencing data alignment with Bowtie2 ---
   # Assume input_deep_seq.fastq is your deep sequencing data (single-end)
   # For demonstration, create a dummy fastq file:
   # echo "@read1\nAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCT\n+\nIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII" > input_deep_seq.fastq
   
   bowtie2 -x reference_hg19/hg19_index_bowtie2 -p 8 -U input_deep_seq.fastq -S output_deep_seq.sam

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3. 3

   Two re-sequenced files : sgRNA2_1_Replicate1 and sgRNA3_1_Replicate1 were merged with sgRNA2_Replicate1 and sgRNA3_Replicate1 respectively

   cat (Inferred with models/gemini-2.5-flash) vN/A

   $ Bash example

   # Merge sgRNA2 files
   cat sgRNA2_1_Replicate1.fastq sgRNA2_Replicate1.fastq > sgRNA2_merged.fastq
   
   # Merge sgRNA3 files
   cat sgRNA3_1_Replicate1.fastq sgRNA3_Replicate1.fastq > sgRNA3_merged.fastq

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4. 4

   Peaks were called by using MACS14 tool for HA-ChIP (-c=Control_dCas9only -p=1e-6)

   MACS v1.4 GitHub

   $ Bash example

   # Install MACS14 (MACS 1.4.2 is often referred to as MACS14)
   # conda install -c bioconda macs=1.4.2
   
   # Define input files and parameters
   # Placeholder: Replace with actual treatment and control BAM files
   HA_CHIP_BAM="HA_ChIP.bam"
   CONTROL_BAM="Control_dCas9only.bam"
   
   # Placeholder: Define genome size (e.g., 'hs' for human, 'mm' for mouse, or a specific number)
   # For a specific number, e.g., 2.7e9 for human, use -g 2.7e9
   GENOME_SIZE="hs"
   
   # Output prefix for MACS files
   OUTPUT_PREFIX="HA_ChIP_peaks"
   
   # Run MACS14 peak calling
   # -t: Treatment file (ChIP-seq data)
   # -c: Control file (Input DNA or IgG control)
   # -f: Format of input files (e.g., 'BAM', 'BED', 'ELAND', 'BOWTIE', 'SAM', 'TAGS')
   # -g: Genome size (e.g., 'hs' for human, 'mm' for mouse, or a specific number)
   # -n: Name of the experiment, which will be used as a prefix for output files
   # -p: P-value cutoff for peak detection
   macs14 -t "${HA_CHIP_BAM}" -c "${CONTROL_BAM}" -f BAM -g "${GENOME_SIZE}" -n "${OUTPUT_PREFIX}" -p 1e-6

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5. 5

   Further data processing steps have been described in the method sections.

   (Inferred with models/gemini-2.5-flash) v(Inferred with models/gemini-2.5-flash)

   $ Bash example

   # No specific command can be inferred from the generic description: 'Further data processing steps have been described in the method sections.'

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