GSE262542 Processing Pipeline

Publication

RNA (New York, N.Y.) (2025) — PMID 40399037

Dataset

Evaluation of novel computational methods that identify RNA-binding protein footprints from structural data

1. 1

   data processing was done using the Skipper pipeline, freelly available at https://github.com/yeolab/skipper.

   Skipper vNot specified GitHub

   $ Bash example

   # Clone the Skipper pipeline repository
   git clone https://github.com/yeolab/skipper.git
   cd skipper
   
   # The Skipper pipeline is a Snakemake workflow that requires a 'config.yaml' file
   # and input data (e.g., FASTQ files) to be present in the working directory.
   # The 'config.yaml' typically specifies parameters such as the reference genome (e.g., hg38 for human eCLIP).
   # Users need to prepare these files according to the pipeline's documentation.
   
   # Execute the Skipper pipeline using Snakemake.
   # The --cores flag specifies the number of CPU cores to use.
   # The --use-conda flag enables Snakemake to manage software environments via Conda.
   snakemake --cores 8 --use-conda

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2. 2

   Adapters trimming was done with Skewer

   Skewer v0.2.2 GitHub

   $ Bash example

   # Install Skewer (example using conda)
   # conda install -c bioconda skewer
   
   # Define input and output file names
   INPUT_FASTQ="input.fastq.gz"
   OUTPUT_PREFIX="trimmed_reads"
   ADAPTER_FILE="adapters.fa" # Placeholder for adapter sequences file (e.g., containing Illumina adapters)
   
   # Execute Skewer for adapter trimming
   # Parameters are based on common eCLIP settings (e.g., from yeolab/eclip workflow)
   # -x: Adapter sequences file
   # -l: Minimum read length after trimming (default 18 in eclip workflow)
   # -q: Minimum quality score to trim (default 20 in eclip workflow)
   # -m: Minimum overlap length for adapter detection (default 1 in eclip workflow)
   # -o: Output file prefix
   skewer -x "${ADAPTER_FILE}" -l 18 -q 20 -m 1 -o "${OUTPUT_PREFIX}" "${INPUT_FASTQ}"

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3. 3

   proccessed reads were mapped with STAR (2.7.10a_alpha_220314)

   STAR v2.7.10a GitHub

   $ Bash example

   # Install STAR (if not already installed)
   # conda install -c bioconda star
   
   # Placeholder for reference genome directory
   # Replace with your actual STAR genome index path (e.g., for hg38)
   GENOME_DIR="/path/to/STAR_genome_index/hg38"
   
   # Placeholder for input FASTQ files
   # Replace with your actual input FASTQ files (e.g., processed reads)
   # Assuming paired-end reads, adjust for single-end if necessary
   READS_R1="processed_reads_R1.fastq.gz"
   READS_R2="processed_reads_R2.fastq.gz"
   
   # Placeholder for output directory and prefix
   OUTPUT_DIR="star_mapping_output"
   OUTPUT_PREFIX="${OUTPUT_DIR}/aligned_reads"
   
   # Create output directory if it doesn't exist
   mkdir -p "${OUTPUT_DIR}"
   
   # Run STAR alignment
   # Common parameters for RNA-seq alignment
   STAR --genomeDir "${GENOME_DIR}" \
        --readFilesIn "${READS_R1}" "${READS_R2}" \
        --runThreadN 8 \
        --outFileNamePrefix "${OUTPUT_PREFIX}" \
        --outSAMtype BAM SortedByCoordinate \
        --outSAMattributes Standard \
        --outFilterMultimapNmax 20 \
        --outFilterMismatchNmax 999 \
        --outFilterMismatchNoverLmax 0.1 \
        --alignIntronMin 20 \
        --alignIntronMax 1000000 \
        --alignMatesGapMax 1000000 \
        --readFilesCommand zcat

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4. 4

   PCR bias was removed using UMIcollapse

   UMIcollapse vN/A

   $ Bash example

   # UMIcollapse is a Python script. Ensure Python is installed.
   # Download the script (if not already available in your environment):
   # wget https://raw.githubusercontent.com/MikeDacre/UMIcollapse/master/UMIcollapse.py
   # chmod +x UMIcollapse.py
   
   # Example usage:
   # Replace input.bam with your actual input BAM file containing UMIs.
   # Replace output.bam with your desired output deduplicated BAM file.
   python UMIcollapse.py -i input.bam -o output.bam

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Tools Used