GSE203091 Processing Pipeline

Publication

The Journal of experimental medicine (2022) — PMID 35593887

Dataset

The long noncoding RNA Malat1 regulates CD8+ T cell differentiation by mediating epigenetic repression (CUT&RUN)

1. 1

   Reads were mapped either to the mouse or yeast genome with bowtie2 -p 8 --local --very-sensitive-local --no-unal --no-mixed --no-discordant --phred33 -I 10 -X 700 (v1.2.2).

   Bowtie2 v1.2.2 GitHub

   $ Bash example

   # Install Bowtie2 (if not already installed)
   # conda install -c bioconda bowtie2
   
   # Placeholder for genome index path and prefix
   # Replace 'path/to/genome_index/index_prefix' with the actual path to your chosen genome index (e.g., mouse or yeast).
   # For example, for mouse (mm10), it might be 'path/to/mm10_index/mm10'
   # For yeast (sacCer3), it might be 'path/to/sacCer3_index/sacCer3'
   GENOME_INDEX="path/to/genome_index/index_prefix"
   
   # Placeholder for input FASTQ files (assuming paired-end based on -I and -X parameters)
   # Replace 'reads_R1.fastq' and 'reads_R2.fastq' with your actual input read files.
   READS_R1="reads_R1.fastq"
   READS_R2="reads_R2.fastq"
   
   # Placeholder for output SAM file
   OUTPUT_SAM="mapped_reads.sam"
   
   bowtie2 \
     -p 8 \
     --local \
     --very-sensitive-local \
     --no-unal \
     --no-mixed \
     --no-discordant \
     --phred33 \
     -I 10 \
     -X 700 \
     -x "${GENOME_INDEX}" \
     -1 "${READS_R1}" \
     -2 "${READS_R2}" \
     -S "${OUTPUT_SAM}"

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2. 2

   Mapped reads were then converted into bed files.

   bedtools (Inferred with models/gemini-2.5-flash) v2.31.0 GitHub

   $ Bash example

   # Install bedtools (if not already installed)
   # conda install -c bioconda bedtools
   
   # Convert mapped reads (BAM format) to BED format.
   # Replace 'input.bam' with the path to your mapped reads BAM file.
   # Replace 'output.bed' with the desired name for your output BED file.
   bedtools bamtobed -i input.bam > output.bed

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3. 3

   A scale factor for Spike-In normalization was calculated for each sample by dividing 1000 from the sequencing depth of mapped yeast reads.

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

   $ Bash example

   # Example: Assume yeast_mapped_reads.txt contains the total count of mapped yeast reads.
   # This file would typically be generated by a previous step, such as mapping reads to a yeast spike-in genome and counting them.
   # Example content of yeast_mapped_reads.txt:
   # 12345678
   
   # Read the sequencing depth of mapped yeast reads from a file or variable
   # For demonstration, we assume it's in 'yeast_mapped_reads.txt'
   YEAST_READ_DEPTH=$(cat yeast_mapped_reads.txt)
   
   # Calculate the scale factor by dividing the sequencing depth by 1000
   # Using awk for floating-point division to ensure precision
   SCALE_FACTOR=$(echo "$YEAST_READ_DEPTH" | awk '{print $1 / 1000}')
   
   # Output the calculated scale factor (e.g., to a file or stdout for subsequent steps)
   echo "$SCALE_FACTOR" > spike_in_scale_factor.txt
   
   echo "Calculated Spike-In Normalization Scale Factor: $SCALE_FACTOR"

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4. 4

   Bed files were normalized using bedtools genomcov (v2.29.2) with the respective scaling factor calculated as describe above.

   bedtools v2.29.2 GitHub

   $ Bash example

   # Install bedtools (if not already installed)
   # conda install -c bioconda bedtools=2.29.2
   
   # Define input and output file paths and the scaling factor
   INPUT_BED="input.bed"
   GENOME_SIZES="hg38.chrom.sizes" # Placeholder: Replace with your actual genome chrom.sizes file
   SCALING_FACTOR="1.0" # Placeholder: Replace with the calculated scaling factor
   OUTPUT_BEDGRAPH="normalized_coverage.bedgraph"
   
   # Normalize bed file coverage using bedtools genomcov with the specified scaling factor
   # The -bg flag outputs coverage in bedGraph format.
   bedtools genomcov -i "${INPUT_BED}" -g "${GENOME_SIZES}" -bg -scale "${SCALING_FACTOR}" > "${OUTPUT_BEDGRAPH}"

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5. 5

   Peaks calls were determined using macs2 macs2 callpeak -broad --broad-cutoff 0.1 -B --nomodel --keep-dup all -f BED (v2.1.2).

   MACS2 v2.1.2 GitHub

   $ Bash example

   # Install MACS2 if not already installed
   # conda install -c bioconda macs2
   
   # Example usage of macs2 callpeak
   # Replace 'treatment.bed', 'control.bed', 'genome_size', and 'output_prefix' with actual values.
   # 'genome_size' can be 'hs' for human, 'mm' for mouse, or a specific number (e.g., 2.7e9 for human).
   macs2 callpeak -t treatment.bed -c control.bed -f BED -g genome_size -n output_prefix -broad --broad-cutoff 0.1 -B --nomodel --keep-dup all

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6. 6

   none provided by the submitter

   (Inferred with models/gemini-2.5-flash)

Tools Used