GSE266924 Processing Pipeline

Publication

Genome biology (2024) — PMID 38807229

Dataset

Exonuclease assisted mapping of protein-RNA interactions (ePRINT)

1. 1

   Read1 data was processed using Skipper, available at: https://github.com/yeolab/skipper

   Skipper vlatest (based on Snakemake workflow) GitHub

   $ Bash example

   # Install Snakemake (if not already installed)
   # conda install -c bioconda -c conda-forge snakemake
   
   # Clone the Skipper workflow repository
   # git clone https://github.com/yeolab/skipper.git
   # cd skipper
   
   # Create a Conda environment from the provided environment.yaml (optional, but recommended for reproducibility)
   # conda env create -f environment.yaml
   # conda activate skipper_env
   
   # Assuming 'Read1.fastq.gz' is the input file for Read1 data
   # The Skipper workflow is designed to be run with a configuration file (config.yaml) and a samplesheet.tsv
   # For a single Read1 file, you would typically define it in a samplesheet and configure the workflow.
   # Example of a minimal samplesheet.tsv (assuming single-end reads):
   # sample_id	fastq_r1
   # my_sample	Read1.fastq.gz
   
   # Example of a minimal config.yaml (adjust parameters as needed):
   # outdir: results
   # genome: hg38 # Placeholder, replace with actual genome if known
   # annotation: gencode.v38 # Placeholder, replace with actual annotation if known
   # adapters:
   #   - AGATCGGAAGAGCACACGTCTGAACTCCAGTCA
   #   - AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
   
   # To run the Skipper workflow for processing Read1 data:
   # snakemake --use-conda --cores 8 --configfile config.yaml --samplesheet samplesheet.tsv all

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2. 2

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

   eCLIP v0.2.2 GitHub

   $ Bash example

   # Install skewer (if not already installed)
   # conda install -c bioconda skewer
   
   # Define input and output files
   INPUT_FASTQ="sample.fastq.gz"
   OUTPUT_PREFIX="sample_trimmed"
   ADAPTER_SEQUENCE="AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC" # Illumina TruSeq 3' adapter, commonly used in eCLIP
   MIN_READ_LENGTH=18 # Minimum read length after trimming, common for eCLIP
   MIN_QUALITY=20     # Minimum average quality score
   THREADS=8          # Number of threads to use
   
   # Trim adapter and barcode sequences using skewer
   # -x: adapter sequence to trim
   # -l: minimum read length after trimming
   # -q: minimum average quality score
   # -t: number of threads
   # -o: output file prefix
   skewer -x "${ADAPTER_SEQUENCE}" -l "${MIN_READ_LENGTH}" -q "${MIN_QUALITY}" -t "${THREADS}" -o "${OUTPUT_PREFIX}" "${INPUT_FASTQ}"
   
   # The output file for single-end reads will be named like sample_trimmed-trimmed.fastq.gz

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3. 3

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

   fastp v0.23.2 (Inferred with models/gemini-2.5-flash)

   $ Bash example

   # fastp installation (example using conda)
   # conda install -c bioconda fastp
   
   # Define input and output file names
   # Replace with actual raw sequencing read files (e.g., from a sequencing run)
   READ1_IN="raw_read1.fastq.gz"
   READ2_IN="raw_read2.fastq.gz" # Remove this line and corresponding -I/-O if single-end reads
   
   # Define output file names for UMI-extracted and quality-controlled reads
   READ1_OUT="umi_extracted_read1.fastq.gz"
   READ2_OUT="umi_extracted_read2.fastq.gz" # Remove this line and corresponding -I/-O if single-end reads
   
   # Define UMI parameters
   # UMI_LOCATION: Specifies where the UMI is located. Common options include:
   #   'per_read': UMI is at the beginning of each read (e.g., Read 1).
   #   'read1': UMI is at the beginning of Read 1.
   #   'read2': UMI is at the beginning of Read 2.
   #   'index1': UMI is in the first index read.
   #   'index2': UMI is in the second index read.
   # UMI_LENGTH: The length of the UMI in base pairs. This value is critical and depends on the library preparation protocol.
   # The following values are placeholders and MUST be replaced with the actual experimental parameters.
   UMI_LOCATION="per_read" # Example: UMI is at the beginning of Read 1
   UMI_LENGTH=10           # Example: UMI is 10 bp long
   
   # Run fastp for UMI extraction, quality control, and adapter trimming
   fastp \
     -i "${READ1_IN}" \
     -o "${READ1_OUT}" \
     -I "${READ2_IN}" \
     -O "${READ2_OUT}" \
     --umi_loc "${UMI_LOCATION}" \
     --umi_len "${UMI_LENGTH}" \
     --json "fastp_report.json" \
     --html "fastp_report.html"
   

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4. 4

   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 v2.7.10a GitHub

   $ Bash example

   # Install STAR (if not already installed)
   # conda install -c bioconda star
   
   # Example: Create a dummy STAR genome directory (replace with your actual path)
   # mkdir -p /path/to/STAR_genome_dir
   # echo "This is a placeholder for STAR genome files." > /path/to/STAR_genome_dir/genome.fa
   
   # Example: Create a dummy input FASTQ file (assuming gzipped based on 'zcat')
   # echo "@read1\nAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCT\n+\n################################" | gzip > input.fq.gz
   
   STAR \
     --alignEndsType EndToEnd \
     --genomeDir /path/to/STAR_genome_dir \
     --genomeLoad NoSharedMemory \
     --outBAMcompression 10 \
     --outFileNamePrefix aligned_reads \
     --winAnchorMultimapNmax 100 \
     --outFilterMultimapNmax 100 \
     --outFilterMultimapScoreRange 1 \
     --outSAMmultNmax 1 \
     --outMultimapperOrder Random \
     --outFilterScoreMin 10 \
     --outFilterType BySJout \
     --limitOutSJcollapsed 5000000 \
     --outReadsUnmapped None \
     --outSAMattrRGline ID:sample1_rep1 \
     --outSAMattributes All \
     --outSAMmode Full \
     --outSAMtype BAM Unsorted \
     --outSAMunmapped Within \
     --readFilesCommand zcat \
     --outStd Log \
     --readFilesIn input.fq.gz \
     --runMode alignReads \
     --runThreadN 8

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5. 5

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

   Skipper vNot specified GitHub

   $ Bash example

   # This code block assumes you have cloned the Skipper repository and are in its root directory.
   # git clone https://github.com/yeolab/skipper.git
   # cd skipper
   
   # It also assumes you have Snakemake installed and configured, or that you will use
   # Snakemake's --use-conda feature to manage environments for the workflow's tools.
   # Example Snakemake installation:
   # conda create -n snakemake_env snakemake -y
   # conda activate snakemake_env
   
   # Before running, you would typically configure the workflow by editing
   # config/config.yaml and config/samples.tsv to specify your input files, genome assembly
   # (e.g., hg38), and other parameters relevant to reproducible enriched windows
   # (IDR) and repetitive element analysis.
   # cp config/config.yaml.example config/config.yaml
   # cp config/samples.tsv.example config/samples.tsv
   # (Edit config/config.yaml and config/samples.tsv with your data and settings)
   
   # Execute the Skipper Snakemake workflow.
   # This command will trigger the analysis steps, including those for
   # identifying reproducible enriched windows (IDR) and analyzing repetitive elements,
   # based on your configuration.
   snakemake --cores 8 --use-conda --snakefile workflow/Snakefile

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Tools Used