GSE271652 Processing Pipeline

Publication

Cell reports (2024) — PMID 39154337

Dataset

Autism-associated CHD8 controls reactive gliosis and neuroinflammation via remodeling chromatin in astrocytes [ATAC-seq]

1. 1

   Reads were downsampled to an equivalent number of reads per sample using seqtk sample (version 1.2)

   seqtk sample v1.2 GitHub

   $ Bash example

   # Install seqtk if not already available
   # conda install -c bioconda seqtk
   
   # Define input and output files (placeholders)
   INPUT_FASTQ="sample_R1.fastq.gz" # Replace with your actual input FASTQ file
   OUTPUT_FASTQ="sample_downsampled_R1.fastq.gz" # Replace with your desired output FASTQ file
   
   # Determine the target number of reads.
   # This typically involves finding the minimum read count across all samples
   # and using that as the target for downsampling all samples to an equivalent number.
   # For example, if the minimum read count across all samples is 10,000,000:
   TARGET_READ_COUNT="10000000" # Placeholder: Replace with the actual target read count
   
   # Downsample reads using seqtk sample
   # -s11 sets a random seed for reproducibility
   seqtk sample -s11 "${INPUT_FASTQ}" "${TARGET_READ_COUNT}" > "${OUTPUT_FASTQ}"

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2. 2

   Adaptors were trimmed with trimmomatic (version 0.39) [1] with the options ILLUMINACLIP:Trimmomatic-0.39/adapters/NexteraPE-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:25 MINLEN:24

   Trimmomatic v0.39

   $ Bash example

   # Install Trimmomatic (if not already installed)
   # wget http://www.usadellab.org/cms/uploads/supplementary/Trimmomatic/Trimmomatic-0.39.zip
   # unzip Trimmomatic-0.39.zip
   # TRIMMOMATIC_DIR="$(pwd)/Trimmomatic-0.39"
   
   # Define input and output file paths (placeholders)
   # Replace with actual input/output file names
   INPUT_R1="input_R1.fastq.gz"
   INPUT_R2="input_R2.fastq.gz"
   OUTPUT_R1_PAIRED="output_R1_paired.fastq.gz"
   OUTPUT_R1_UNPAIRED="output_R1_unpaired.fastq.gz"
   OUTPUT_R2_PAIRED="output_R2_paired.fastq.gz"
   OUTPUT_R2_UNPAIRED="output_R2_unpaired.fastq.gz"
   
   # Define Trimmomatic JAR path and adapter file path
   # Adjust TRIMMOMATIC_JAR and ADAPTER_FILE paths if Trimmomatic is installed elsewhere
   TRIMMOMATIC_JAR="Trimmomatic-0.39/trimmomatic-0.39.jar"
   ADAPTER_FILE="Trimmomatic-0.39/adapters/NexteraPE-PE.fa"
   
   # Run Trimmomatic
   java -jar "${TRIMMOMATIC_JAR}" PE \
     "${INPUT_R1}" "${INPUT_R2}" \
     "${OUTPUT_R1_PAIRED}" "${OUTPUT_R1_UNPAIRED}" \
     "${OUTPUT_R2_PAIRED}" "${OUTPUT_R2_UNPAIRED}" \
     ILLUMINACLIP:"${ADAPTER_FILE}":2:30:10 \
     LEADING:3 TRAILING:3 SLIDINGWINDOW:4:25 MINLEN:24

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3. 3

   Reads were then aligned to the mouse genome (mm10) with bowtie2 (version 2.4.4) using default parameters

   Bowtie2 v2.4.4 GitHub

   $ Bash example

   # Install Bowtie2 (if not already installed)
   # conda install -c bioconda bowtie2=2.4.4
   
   # Placeholder for Bowtie2 index for mouse genome (mm10)
   # Ensure the index is built and available at this path.
   # Example: bowtie2-build /path/to/mm10.fa /path/to/bowtie2_indices/mm10_index
   MM10_INDEX_PREFIX="/path/to/bowtie2_indices/mm10_index"
   
   # Placeholder for input FASTQ file(s)
   # For single-end reads:
   INPUT_FASTQ="input_reads.fastq" # Replace with your actual input FASTQ file
   
   # For paired-end reads (uncomment and modify if applicable):
   # INPUT_FASTQ_R1="input_reads_R1.fastq"
   # INPUT_FASTQ_R2="input_reads_R2.fastq"
   
   # Placeholder for output SAM file
   OUTPUT_SAM="aligned_reads.sam" # Replace with your desired output SAM file
   
   # Align reads to the mouse genome (mm10) with bowtie2 (version 2.4.4) using default parameters
   # -x: specifies the index prefix
   # -U: specifies unpaired reads (use -1 <reads_1.fastq> -2 <reads_2.fastq> for paired-end)
   # -S: specifies the output SAM file
   # Default parameters are used as stated in the description.
   bowtie2 -x "${MM10_INDEX_PREFIX}" -U "${INPUT_FASTQ}" -S "${OUTPUT_SAM}"
   
   # If using paired-end reads, use the following command instead:
   # bowtie2 -x "${MM10_INDEX_PREFIX}" -1 "${INPUT_FASTQ_R1}" -2 "${INPUT_FASTQ_R2}" -S "${OUTPUT_SAM}"
   
   # Optional: Convert SAM to BAM and sort (requires samtools)
   # samtools view -bS "${OUTPUT_SAM}" | samtools sort -o "aligned_reads.bam"
   # samtools index "aligned_reads.bam"

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4. 4

   Duplicates were removed with picard through gatk MarkDuplicates (version 4.2.5.0)

   Picard v4.2.5.0

   $ Bash example

   # Install GATK (which includes Picard tools like MarkDuplicates)
   # conda install -c bioconda gatk4
   
   # Run MarkDuplicates to remove duplicates
   # -I: Input BAM file
   # -O: Output BAM file with duplicates removed
   # -M: File to write duplication metrics to
   gatk MarkDuplicates \
     -I input.bam \
     -O output.bam \
     -M metrics.txt

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5. 5

   Peak detection was performed using macs2 callpeak (version 2.2.7.1) with the parameters “-g mm --qvalue 0.05 --shift 100 --extsize 200 --format BAMPE --keep-dup=all --cutoff-analysis –bdg’’.

   MACS2 v2.2.7 GitHub

   $ Bash example

   # Install MACS2 if not already installed
   # conda install -c bioconda macs2
   
   # Placeholder for input files and output prefix
   # Replace treatment.bam with your actual treatment BAM file
   # Replace control.bam with your actual control BAM file (if applicable, MACS2 can run without control)
   # Replace output_prefix with your desired output file prefix
   
   macs2 callpeak -t treatment.bam -c control.bam -f BAMPE -g mm -n output_prefix --qvalue 0.05 --shift 100 --extsize 200 --keep-dup=all --cutoff-analysis --bdg

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Tools Used