GSE124071 Processing Pipeline

Publication

Cell stem cell (2019) — PMID 31588046

Dataset

The RNA helicase DDX6 regulates self-renewal and differentiation of human and mouse stem cells [ATAC-Seq]

1. 1

   Illumina Casava1.7 software used for basecalling.

   Illumina Casava v1.7 GitHub

   $ Bash example

   # Illumina Casava 1.7 software was used for basecalling and demultiplexing,
   # converting BCL files generated by the sequencer into FASTQ files.
   # The 'configureBclToFastq.pl' script was a key component of the Casava 1.7 pipeline.
   
   # This command is a placeholder demonstrating the typical usage of configureBclToFastq.pl
   # within the Casava 1.7 pipeline. Actual paths and sample sheet would vary based on the specific run.
   
   # Define placeholder paths for an Illumina run directory structure
   BCL_INPUT_DIR="/path/to/illumina_run/Data/Intensities/BaseCalls/"
   SAMPLE_SHEET="/path/to/illumina_run/SampleSheet.csv"
   OUTPUT_DIR="/path/to/output/fastq/"
   
   # Ensure output directory exists
   mkdir -p "${OUTPUT_DIR}"
   
   # Execute the bcl to fastq conversion using configureBclToFastq.pl
   # Note: 'configureBclToFastq.pl' is part of the Casava 1.7 suite and needs to be in the system's PATH
   # or called with its full path.
   configureBclToFastq.pl --input-dir "${BCL_INPUT_DIR}" \
                          --output-dir "${OUTPUT_DIR}" \
                          --sample-sheet "${SAMPLE_SHEET}" \
                          --force

   Copy View on GitHub
2. 2

   Sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence, then mapped to reference genome hg19 using BWA

   BWA vBWA-MEM GitHub

   $ Bash example

   # Install necessary tools (if not already installed)
   # conda install -c bioconda bwa samtools fastp
   
   # Define reference genome path and name
   REF_GENOME_DIR="./reference"
   REF_GENOME_NAME="hg19"
   REF_GENOME_FA="${REF_GENOME_DIR}/${REF_GENOME_NAME}.fa"
   REF_GENOME_URL="http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz"
   
   # Create reference directory if it doesn't exist
   mkdir -p "${REF_GENOME_DIR}"
   
   # Download and decompress hg19 reference genome if not present
   if [ ! -f "${REF_GENOME_FA}" ]; then
       echo "Downloading hg19 reference genome..."
       wget -O "${REF_GENOME_DIR}/${REF_GENOME_NAME}.fa.gz" "${REF_GENOME_URL}"
       gunzip "${REF_GENOME_DIR}/${REF_GENOME_NAME}.fa.gz"
   fi
   
   # Index the reference genome for BWA if not already indexed
   if [ ! -f "${REF_GENOME_FA}.bwt" ]; then
       echo "Indexing hg19 reference genome with BWA..."
       bwa index "${REF_GENOME_FA}"
   fi
   
   # Define input and output file names
   INPUT_FASTQ="input.fastq.gz" # Placeholder for your input reads
   TRIMMED_FASTQ="trimmed_reads.fastq.gz"
   ALIGNED_SAM="aligned_reads.sam"
   ALIGNED_BAM="aligned_reads.bam"
   SORTED_BAM="sorted_aligned_reads.bam"
   
   # Step 1: Trim adaptor sequences and mask low-quality/low-complexity sequences using fastp
   # This step addresses "trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence"
   echo "Trimming and quality filtering reads with fastp..."
   fastp -i "${INPUT_FASTQ}" -o "${TRIMMED_FASTQ}" \
         --detect_adapter_for_pe \
         --trim_poly_g \
         --trim_poly_x \
         --low_complexity_filter \
         --qualified_quality_phred 15 \
         --length_required 30 \
         --json "${TRIMMED_FASTQ%.fastq.gz}.json" \
         --html "${TRIMMED_FASTQ%.fastq.gz}.html"
   
   # Step 2: Map trimmed reads to reference genome hg19 using BWA-MEM
   echo "Mapping reads to hg19 with BWA-MEM..."
   bwa mem "${REF_GENOME_FA}" "${TRIMMED_FASTQ}" > "${ALIGNED_SAM}"
   
   # Step 3: Convert SAM to BAM, sort, and index the BAM file
   echo "Converting SAM to BAM, sorting, and indexing..."
   samtools view -bS "${ALIGNED_SAM}" | samtools sort -o "${SORTED_BAM}" -
   samtools index "${SORTED_BAM}"
   
   echo "Pipeline completed. Sorted BAM file: ${SORTED_BAM}"

   Copy View on GitHub
3. 3

   peaks were called using HOTSPOT with default parameter

   HOTSPOT vUnspecified (Inferred with models/gemini-2.5-flash)

   $ Bash example

   # Install hotspot (example, adjust based on availability)
   # conda install -c bioconda hotspot # If available via Bioconda
   # Or clone from GitHub and install dependencies:
   # git clone https://github.com/ENCODE-DCC/hotspot.git
   # cd hotspot
   # python setup.py install # Or just run the script directly
   
   # Placeholder for input BED file (e.g., from aligned reads, pre-processed for signal).
   # Replace 'input.bed' with the actual path to your input BED file.
   # Replace 'hg38.chrom.sizes' with the path to your genome size file.
   # Example hg38.chrom.sizes can be obtained from UCSC:
   # wget -qO- http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes > hg38.chrom.sizes
   
   # Run HOTSPOT with default parameters
   # Assuming hotspot.py is in your PATH or specified with its full path
   hotspot.py -i input.bed -o hotspot_peaks.bed -g hg38.chrom.sizes

   Copy