GSE203090 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 (ChIP-Seq)

1. 1

   Libraries were filtered and mapped to the mm10 genome using ENCODE Transcription Factor and Histone ChIP-Seq processing pipeline with default parameters for histone marks (github.com/ENCODE-DCC/chip-seq-pipeline2).

   ChIP-seq vdefault GitHub

   $ Bash example

   # Install Cromwell (requires Java). Replace X.Y.Z with the actual version.
   # java -jar cromwell-X.Y.Z.jar install
   
   # Clone the ENCODE ChIP-seq pipeline repository if not already present
   # git clone https://github.com/ENCODE-DCC/chip-seq-pipeline2.git
   # cd chip-seq-pipeline2
   
   # Create an inputs JSON file specifying the parameters based on the description.
   # This includes the genome assembly (mm10) and pipeline type (histone).
   # Replace 'path/to/your/fastq_R1.fastq.gz' and 'path/to/your/fastq_R2.fastq.gz' with actual input FASTQ files.
   # Replace 'path/to/mm10_genome.tsv' with the path to your genome TSV file for mm10.
   # The genome TSV file typically contains paths to the genome FASTA, BWA index, blacklist regions, etc.,
   # and can be found in the pipeline's 'genome_data' directory or generated.
   cat << EOF > inputs.json
   {
     "chip.pipeline_type": "histone",
     "chip.genome_tsv": "path/to/mm10_genome.tsv",
     "chip.fastqs_rep1_R1": ["path/to/your/fastq_R1.fastq.gz"],
     "chip.fastqs_rep1_R2": ["path/to/your/fastq_R2.fastq.gz"],
     "chip.paired_end": true,
     "chip.auto_detect_adapter": true,
     "chip.trim_adapter": true,
     "chip.multimapping": 0,
     "chip.nthr": 8,
     "chip.mem_gb": 32
     // Other default parameters for histone marks are handled by the pipeline's internal logic
   }
   EOF
   
   # Execute the ENCODE ChIP-seq pipeline using Cromwell.
   # Ensure you are in the directory containing 'chip.wdl' and 'inputs.json'.
   # Replace 'cromwell-X.Y.Z.jar' with the actual Cromwell JAR file name.
   java -jar cromwell-X.Y.Z.jar run chip.wdl -i inputs.json

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2. 2

   Final pooled bigwig files were used for visualization.

   UCSC tools vv443 GitHub

   $ Bash example

   # Install UCSC tools (if not already installed)
   # conda install -c bioconda ucsc-bedgraphtobigwig
   
   # Define input and output files
   # INPUT_BEDGRAPH: This would be the pooled and normalized bedGraph file generated in a previous step.
   INPUT_BEDGRAPH="final_pooled.bedGraph"
   # CHROM_SIZES: Chromosome sizes file for the reference genome (e.g., hg38).
   CHROM_SIZES="hg38.chrom.sizes"
   OUTPUT_BIGWIG="final_pooled.bigWig"
   
   # Download chrom.sizes for hg38 (if not available)
   # wget -O ${CHROM_SIZES} http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes
   
   # Convert bedGraph to bigWig format for efficient visualization on the UCSC Genome Browser
   bedGraphToBigWig "${INPUT_BEDGRAPH}" "${CHROM_SIZES}" "${OUTPUT_BIGWIG}"

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3. 3

   none provided by the submitter

   N/A (No step description provided) (Inferred with models/gemini-2.5-flash) vN/A (No step description provided)

   $ Bash example

   # No step description was provided, so specific tools, parameters,
   # and reference datasets cannot be inferred.
   # Please provide a step description to generate a relevant code block.

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Tools Used