GSE176060 Processing Pipeline

Publication

Nature communications (2021) — PMID 34732726

Dataset

RNA-Seq of isogenic human iPS cell-derived cardiomyocytes with RBM20 mutations created by genome editing (eCLIP)

1. 1

   library strategy: eCLIP

   eCLIP vv0.1.0 GitHub

   $ Bash example

   # Assuming you have cloned the skipper repository and are in its root directory:
   # git clone https://github.com/yeolab/skipper.git
   # cd skipper
   
   # Create a placeholder configuration file (config.yaml)
   # This file defines parameters, input/output directories, and reference genome paths.
   # Replace placeholder paths with actual paths to your data and reference files.
   cat << EOF > config.yaml
   # General settings
   output_dir: results
   threads: 8
   
   # Reference genome settings (example for human hg38)
   genome_build: hg38
   genome_fasta: /path/to/reference/hg38.fa
   genome_gtf: /path/to/reference/gencode.v38.annotation.gtf
   genome_star_index: /path/to/reference/STAR_index_hg38
   genome_chrom_sizes: /path/to/reference/hg38.chrom.sizes
   genome_blacklist: /path/to/reference/hg38_blacklist.bed
   
   # Adapter sequences for trimming (example)
   adapters_fasta: /path/to/adapters/truseq_adapters.fa
   
   # Peak calling parameters (clipper)
   clipper_min_read_length: 15
   clipper_window_size: 20
   clipper_step_size: 1
   clipper_fdr_threshold: 0.05
   
   # IDR parameters (merge_peaks)
   idr_threshold: 0.05
   
   # Other tool-specific parameters can be added here
   EOF
   
   # Create a placeholder samplesheet (samples.tsv)
   # This file lists your eCLIP and input samples, their FASTQ files, and metadata.
   # Replace placeholder paths with actual paths to your FASTQ files.
   cat << EOF > samples.tsv
   sample_id	fastq_r1	fastq_r2	antibody	replicate	condition
   eCLIP_sample1_rep1	/path/to/fastq/eCLIP_sample1_rep1_R1.fastq.gz	/path/to/fastq/eCLIP_sample1_rep1_R2.fastq.gz	RBFOX2	1	treatment
   eCLIP_sample1_rep2	/path/to/fastq/eCLIP_sample1_rep2_R1.fastq.gz	/path/to/fastq/eCLIP_sample1_rep2_R2.fastq.gz	RBFOX2	2	treatment
   input_sample1_rep1	/path/to/fastq/input_sample1_rep1_R1.fastq.gz	/path/to/fastq/input_sample1_rep1_R2.fastq.gz	Input	1	treatment
   EOF
   
   # Execute the eCLIP Snakemake workflow using the created config and samplesheet.
   # --use-conda: Automatically creates and manages conda environments for tools.
   # --cores 8: Use 8 CPU cores for parallel execution. Adjust as needed.
   # --configfile config.yaml: Specifies the configuration file.
   # --profile profiles/conda: Uses a predefined profile for conda environment management.
   # Ensure Snakemake is installed and accessible in your PATH.
   # conda install -c conda-forge -c bioconda snakemake
   snakemake -s Snakefile --use-conda --cores 8 --configfile config.yaml --profile profiles/conda

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2. 2

   Reproducible RBM20 peaks (hg19) obtained from replicate WT and R636S HMZ iPSC-CMs compared to size-matched input controls, were used for all down-stream analyses.

   Clipper (Inferred with models/gemini-2.5-flash), merge_peaks (Inferred with models/gemini-2.5-flash) vlatest (Clipper), latest (merge_peaks)

   $ Bash example

   # --- Setup Environment ---
   # It's recommended to use a virtual environment or conda for managing dependencies.
   # For example, to install clipper and its dependencies:
   # conda create -n eclip_env python=3.8
   # conda activate eclip_env
   # pip install numpy scipy pysam
   # git clone https://github.com/yeolab/clipper.git
   # git clone https://github.com/yeolab/merge_peaks.git
   # export PATH=$PATH:$(pwd)/clipper:$(pwd)/merge_peaks # Add scripts to PATH if not installed globally
   
   # --- Define Variables ---
   GENOME="hg19"
   GENOME_SIZE_FILE="${GENOME}.chrom.sizes" # Placeholder for genome size file
   # Download hg19 chrom.sizes if not available
   # wget -O ${GENOME_SIZE_FILE} http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.chrom.sizes
   
   # Input BAM files (placeholders - replace with actual paths)
   # Assuming two replicates for WT and R636S, and two size-matched input controls
   WT_REP1_BAM="WT_iPSC_CM_rep1.bam"
   WT_REP2_BAM="WT_iPSC_CM_rep2.bam"
   R636S_REP1_BAM="R636S_iPSC_CM_rep1.bam"
   R636S_REP2_BAM="R636S_iPSC_CM_rep2.bam"
   INPUT_REP1_BAM="Input_control_rep1.bam"
   INPUT_REP2_BAM="Input_control_rep2.bam"
   
   OUTPUT_DIR="RBM20_peaks_analysis"
   mkdir -p ${OUTPUT_DIR}
   
   # --- 1. Peak Calling with Clipper ---
   # Call peaks for each replicate against its size-matched input control
   
   echo "Calling RBM20 peaks for WT replicates..."
   python clipper/clipper.py -b ${WT_REP1_BAM} -c ${INPUT_REP1_BAM} -s ${GENOME_SIZE_FILE} -o ${OUTPUT_DIR}/WT_rep1_RBM20_peaks.bed
   python clipper/clipper.py -b ${WT_REP2_BAM} -c ${INPUT_REP2_BAM} -s ${GENOME_SIZE_FILE} -o ${OUTPUT_DIR}/WT_rep2_RBM20_peaks.bed
   
   echo "Calling RBM20 peaks for R636S replicates..."
   python clipper/clipper.py -b ${R636S_REP1_BAM} -c ${INPUT_REP1_BAM} -s ${GENOME_SIZE_FILE} -o ${OUTPUT_DIR}/R636S_rep1_RBM20_peaks.bed
   python clipper/clipper.py -b ${R636S_REP2_BAM} -c ${INPUT_REP2_BAM} -s ${GENOME_SIZE_FILE} -o ${OUTPUT_DIR}/R636S_rep2_RBM20_peaks.bed
   
   # --- 2. Identifying Reproducible Peaks with merge_peaks (IDR) ---
   # Perform IDR analysis on replicates for each condition (WT and R636S)
   # A common IDR threshold is 0.05
   
   echo "Performing IDR for WT RBM20 peaks..."
   python merge_peaks/merge_peaks.py -i ${OUTPUT_DIR}/WT_rep1_RBM20_peaks.bed ${OUTPUT_DIR}/WT_rep2_RBM20_peaks.bed -o ${OUTPUT_DIR}/WT_RBM20_reproducible_peaks -t 0.05
   
   echo "Performing IDR for R636S RBM20 peaks..."
   python merge_peaks/merge_peaks.py -i ${OUTPUT_DIR}/R636S_rep1_RBM20_peaks.bed ${OUTPUT_DIR}/R636S_rep2_RBM20_peaks.bed -o ${OUTPUT_DIR}/R636S_RBM20_reproducible_peaks -t 0.05
   
   echo "Reproducible RBM20 peaks for WT and R636S conditions are generated in ${OUTPUT_DIR}/ (look for *_idr_peaks.bed files)"

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3. 3

   Downstream bioinformatics were performed according to the default ENCODE eCLIP bioinformatics pipeline as described at from https://www.encodeproject.org/eclip/.

   eCLIP vSTAR 2.7.10a, CLIPper 1.0.0, merge_peaks 1.0.0 (from yeolab/eclip CWL workflow) GitHub

   $ Bash example

   # Install cwltool (if not already installed)
   # pip install cwltool
   
   # Clone the ENCODE eCLIP CWL workflow repository
   # git clone https://github.com/yeolab/eclip.git
   # cd eclip
   
   # --- Placeholder for reference genome data ---
   # Download human genome (hg38) FASTA, GTF, chromosome sizes, and blacklist regions
   # mkdir -p /path/to/genome_data/hg38
   # cd /path/to/genome_data/hg38
   # wget -c https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz
   # gunzip hg38.fa.gz
   # wget -c https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/genes/hg38.ncbiRefSeq.gtf.gz
   # gunzip hg38.ncbiRefSeq.gtf.gz
   # wget -c https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes
   # wget -c https://raw.githubusercontent.com/Boyle-Lab/Blacklist/master/lists/hg38-blacklist.v2.bed.gz
   # gunzip hg38-blacklist.v2.bed.gz
   
   # --- Placeholder for STAR index generation (if not pre-built) ---
   # mkdir -p /path/to/genome_data/hg38/STAR_index
   # STAR \
   #   --runThreadN 8 \
   #   --runMode genomeGenerate \
   #   --genomeDir /path/to/genome_data/hg38/STAR_index \
   #   --genomeFastaFiles /path/to/genome_data/hg38/hg38.fa \
   #   --sjdbGTFfile /path/to/genome_data/hg38/hg38.ncbiRefSeq.gtf \
   #   --sjdbOverhang 100 # Adjust based on read length - 1
   
   # Define input files and parameters for the eCLIP pipeline
   # Replace with actual paths to your FASTQ files and genome data
   # Assuming single-end reads for simplicity. Adjust for paired-end if needed.
   cat << EOF > eclip_job.yaml
   fastq_rep1_r1:
     class: File
     path: /path/to/your/eclip_rep1.fastq.gz
   fastq_input_r1:
     class: File
     path: /path/to/your/input_control.fastq.gz
   genome_fasta:
     class: File
     path: /path/to/genome_data/hg38/hg38.fa
   genome_gtf:
     class: File
     path: /path/to/genome_data/hg38/hg38.ncbiRefSeq.gtf
   chrom_sizes:
     class: File
     path: /path/to/genome_data/hg38/hg38.chrom.sizes
   blacklist_regions:
     class: File
     path: /path/to/genome_data/hg38/hg38-blacklist.v2.bed
   output_prefix: my_eclip_experiment
   threads: 8
   # Optional parameters (uncomment and adjust as needed)
   # read_length: 50
   # min_read_length: 18
   # max_read_length: 100
   # min_mapq: 20
   # min_peak_width: 5
   # max_peak_width: 500
   # fdr_threshold: 0.05
   # idr_threshold: 0.1
   # min_fold_enrichment: 2.0
   # min_reads_in_peak: 10
   EOF
   
   # Execute the eCLIP CWL workflow using cwltool
   # Ensure you are in the directory containing eclip.cwl or provide its full path
   cwltool /path/to/eclip/eclip.cwl eclip_job.yaml
   

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Tools Used