GSE224998 Processing Pipeline

Publication

Cell genomics (2023) — PMID 37388912

Dataset

RPS19 binding with and without Diamond-Blackfan anemia variants

1. 1

   library strategy: eCLIP

   eCLIP vlatest (pre-2021) GitHub

   $ Bash example

   # Clone the Yeo Lab eCLIP CWL workflow repository
   # git clone https://github.com/yeolab/eclip.git
   # cd eclip
   
   # Define placeholder input parameters for the eCLIP workflow in a YAML file (e.g., inputs.yaml)
   # Users should replace these paths and values with their actual data and desired settings.
   # Refer to the eclip/eclip.cwl and eclip/example_inputs.yaml in the cloned repository for full details.
   cat << EOF > inputs.yaml
   fastq_r1:
     class: File
     path: /path/to/your/sample_R1.fastq.gz # Placeholder: Replace with your R1 FASTQ file
   fastq_r2:
     class: File
     path: /path/to/your/sample_R2.fastq.gz # Placeholder: Replace with your R2 FASTQ file (or omit if single-end)
   genome_fasta:
     class: File
     path: /path/to/your/reference/hg38.fa # Placeholder: Replace with your reference genome FASTA (e.g., hg38)
   genome_gtf:
     class: File
     path: /path/to/your/reference/gencode.v38.annotation.gtf # Placeholder: Replace with your genome annotation GTF (e.g., Gencode v38)
   adapter_sequence: "AGATCGGAAGAGCACACGTCTGAACTCCAGTCA" # Placeholder: Replace with your actual adapter sequence
   output_prefix: "eclip_sample" # Placeholder: Desired prefix for output files
   EOF
   
   # Install cwltool if not already installed
   # pip install cwltool
   
   # Execute the eCLIP CWL workflow using cwltool
   # Ensure you are in the directory containing 'eclip.cwl' (e.g., the 'eclip' directory after cloning)
   cwltool eclip.cwl inputs.yaml

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2. 2

   Data was processed using Skipper, available at: http://github.com/yeolab/skipper

   Skipper vNot specified GitHub

   $ Bash example

   # Skipper is a Snakemake workflow for eCLIP data processing.
   # To use Skipper, first clone the repository:
   # git clone http://github.com/yeolab/skipper
   # cd skipper
   
   # Ensure Snakemake is installed:
   # conda install -c bioconda -c conda-forge snakemake
   
   # A 'config.yaml' file is required to run the Snakemake workflow.
   # This file specifies input data, reference genomes, and other parameters.
   # Example 'config.yaml' content (replace with actual paths and parameters):
   # ---
   # samples:
   #   sample_name_1:
   #     R1: "path/to/sample_1_R1.fastq.gz"
   #     R2: "path/to/sample_1_R2.fastq.gz" # Optional, if paired-end
   #   sample_name_2:
   #     R1: "path/to/sample_2_R1.fastq.gz"
   # genome_assembly: "hg38" # Placeholder for a common human reference genome
   # adapters: "path/to/adapters.fa" # Path to adapter sequences
   # output_dir: "results"
   # ---
   
   # Execute the Skipper Snakemake workflow.
   # Replace '8' with the desired number of CPU cores.
   # Ensure 'config.yaml' is properly configured for your data.
   snakemake --cores 8 --configfile config.yaml

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3. 3

   Reads were trimmed for adapter sequences and barcode sequences (eCLIP samples) using skewer.

   eCLIP v0.2.2 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install skewer (if not already installed)
   # conda install -c bioconda skewer
   
   # Define input and output files
   INPUT_READS="input_reads.fastq.gz"
   OUTPUT_PREFIX="output_trimmed"
   
   # Define eCLIP-specific adapter and barcode sequences
   # This 3' adapter sequence is commonly used in eCLIP protocols (e.g., from NEBNext Small RNA Library Prep Set for Illumina).
   ADAPTER_SEQUENCE="AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC"
   # This represents a generic 8-nucleotide 5' barcode/UMI sequence to be trimmed.
   # In the yeolab/eclip workflow, a 'barcode_sequence' input is passed to skewer's -y option for 5' trimming.
   # If the barcode is a fixed sequence, replace 'NNNNNNNN' with the actual sequence.
   # If it's a UMI, skewer will attempt to match and trim this pattern from the 5' end.
   BARCODE_SEQUENCE="NNNNNNNN"
   
   # Define trimming parameters, based on common eCLIP settings from yeolab/eclip workflow
   MIN_READ_LENGTH=18  # Minimum read length after trimming
   MIN_ADAPTER_LENGTH=10 # Minimum length of adapter to be matched
   QUALITY_THRESHOLD=20 # Minimum quality score for trimming
   THREADS=8 # Number of threads to use
   
   # Execute skewer for adapter and barcode trimming
   skewer -x "${ADAPTER_SEQUENCE}" \
          -y "${BARCODE_SEQUENCE}" \
          -m ${MIN_READ_LENGTH} \
          -l ${MIN_ADAPTER_LENGTH} \
          -q ${QUALITY_THRESHOLD} \
          -t ${THREADS} \
          -o "${OUTPUT_PREFIX}" \
          "${INPUT_READS}"
   
   # The output files will be named output_trimmed-trimmed.fastq.gz (for single-end) or output_trimmed-trimmed-pair1.fastq.gz and output_trimmed-trimmed-pair2.fastq.gz (for paired-end)

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4. 4

   Unique Molecular Identifiers (UMIs) were extracted from raw sequencing reads with fastp

   fastp v0.23.2 GitHub

   $ Bash example

   # Install fastp (e.g., using conda)
   # conda install -c bioconda fastp
   
   # Define input and output file paths
   INPUT_R1="raw_reads_R1.fastq.gz"
   INPUT_R2="raw_reads_R2.fastq.gz"
   OUTPUT_R1="umi_extracted_R1.fastq.gz"
   OUTPUT_R2="umi_extracted_R2.fastq.gz"
   REPORT_JSON="fastp.json"
   REPORT_HTML="fastp.html"
   
   # Extract UMIs from raw sequencing reads using fastp
   # This command assumes UMIs are located at the beginning of Read 1 (e.g., 10 bp long)
   # and moves them to the read ID. Adjust --umi_loc and --umi_len as per experimental design.
   fastp \
     -i "${INPUT_R1}" \
     -o "${OUTPUT_R1}" \
     -I "${INPUT_R2}" \
     -O "${OUTPUT_R2}" \
     --umi \
     --umi_loc read1 \
     --umi_len 10 \
     --umi_prefix UMI: \
     --json "${REPORT_JSON}" \
     --html "${REPORT_HTML}" \
     -w 8

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5. 5

   Extracted reads were aligned using STAR: --alignEndsType EndToEnd --genomeDir {params.star_sjdb} --genomeLoad NoSharedMemory --outBAMcompression 10 --outFileNamePrefix {params.outprefix} --winAnchorMultimapNmax 100 --outFilterMultimapNmax 100 --outFilterMultimapScoreRange 1 --outSAMmultNmax 1 --outMultimapperOrder Random --outFilterScoreMin 10 --outFilterType BySJout --limitOutSJcollapsed 5000000 --outReadsUnmapped None --outSAMattrRGline ID:{wildcards.replicate_label} --outSAMattributes All --outSAMmode Full --outSAMtype BAM Unsorted --outSAMunmapped Within --readFilesCommand zcat --outStd Log --readFilesIn {input.fq} --runMode alignReads --runThreadN {threads}

   STAR v2.7.x GitHub

   $ Bash example

   # Install STAR using conda
   # conda install -c bioconda star
   
   # Example variables (replace with actual paths and values)
   STAR_GENOME_DIR="/path/to/STAR_genome_index/GRCh38" # Path to STAR genome index directory
   OUTPUT_PREFIX="sample_aligned"
   READ_FILES="sample_R1.fastq.gz sample_R2.fastq.gz" # For paired-end reads, space-separated. For single-end, provide one file.
   THREADS=8
   REPLICATE_LABEL="sample_rep1"
   
   STAR \
       --alignEndsType EndToEnd \
       --genomeDir "${STAR_GENOME_DIR}" \
       --genomeLoad NoSharedMemory \
       --outBAMcompression 10 \
       --outFileNamePrefix "${OUTPUT_PREFIX}" \
       --winAnchorMultimapNmax 100 \
       --outFilterMultimapNmax 100 \
       --outFilterMultimapScoreRange 1 \
       --outSAMmultNmax 1 \
       --outMultimapperOrder Random \
       --outFilterScoreMin 10 \
       --outFilterType BySJout \
       --limitOutSJcollapsed 5000000 \
       --outReadsUnmapped None \
       --outSAMattrRGline ID:"${REPLICATE_LABEL}" \
       --outSAMattributes All \
       --outSAMmode Full \
       --outSAMtype BAM Unsorted \
       --outSAMunmapped Within \
       --readFilesCommand zcat \
       --outStd Log \
       --readFilesIn "${READ_FILES}" \
       --runMode alignReads \
       --runThreadN "${THREADS}"

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6. 6

   Custom scripts called reproducible enriched windows and repetitive elements as part of Skipper

   Skipper vunversioned GitHub

   $ Bash example

   # Install Snakemake if not already installed
   # conda install -c bioconda -c conda-forge snakemake
   
   # Clone the Skipper workflow
   # git clone https://github.com/yeolab/skipper.git
   # cd skipper
   
   # Create a configuration file (config.yaml) for your eCLIP experiment.
   # This file defines input samples, genome assembly, and other parameters.
   # Example content for config.yaml (adjust paths and values as needed):
   cat << EOF > config.yaml
   samples:
     sample1:
       ip: "path/to/sample1_ip.bam"
       input: "path/to/sample1_input.bam"
     sample2:
       ip: "path/to/sample2_ip.bam"
       input: "path/to/sample2_input.bam"
   genome: "hg38" # Placeholder: Use the latest assembly like hg38 or mm10
   annotation: "path/to/gencode.vXX.annotation.gtf" # Placeholder: e.g., GENCODE v38 for hg38
   # Other parameters for peak calling, IDR, repeat element analysis, etc.
   # Refer to the Skipper documentation for a complete list of configurable options.
   EOF
   
   # Run the Skipper workflow.
   # This command will execute the Snakemake pipeline, which includes
   # custom scripts for identifying reproducible enriched windows (peaks)
   # and analyzing repetitive elements based on the provided configuration.
   # The specific outputs related to "reproducible enriched windows" and
   # "repetitive elements" will be generated in the designated output directory
   # as defined by the workflow and configuration.
   snakemake --snakefile Snakefile --configfile config.yaml --cores 8 # Adjust cores as needed

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Tools Used