GSE232599 Processing Pipeline

Publication

Nature biotechnology (2024) — PMID 38168984

Dataset

Systematic identification of RNA-binding proteins and tethered domains that activate exon splicing inclusion

1. 1

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

   Skipper vNot specified GitHub

   $ Bash example

   # Install Snakemake (if not already installed)
   # It is recommended to use a dedicated conda environment for Snakemake.
   # conda create -n snakemake snakemake
   # conda activate snakemake
   
   # Clone the Skipper workflow repository
   # git clone https://github.com/yeolab/skipper.git
   # cd skipper
   
   # Create or modify a configuration file (e.g., config.yaml)
   # This file defines input files, reference genome, and other parameters for the workflow.
   # A typical config.yaml for eCLIP using Skipper might look like this:
   #
   # genome: "hg38" # Placeholder for the reference genome (e.g., hg38, mm10). Using hg38 as a common latest assembly.
   # fastq_dir: "/path/to/your/fastq_files" # Directory containing raw FASTQ files
   # output_dir: "skipper_results" # Directory for output files
   # threads: 10 # Number of CPU threads to use
   #
   # samples:
   #   sample_RBP_rep1:
   #     RBP: "YourRBP"
   #     replicate: "rep1"
   #     fastq: ["sample_RBP_rep1_R1.fastq.gz", "sample_RBP_rep1_R2.fastq.gz"] # Adjust for single-end or paired-end
   #     control: "input_control_sample_name" # Name of the corresponding input control sample
   #   input_control_sample_name:
   #     RBP: "Input"
   #     replicate: "rep1"
   #     fastq: ["input_control_sample_name_R1.fastq.gz", "input_control_sample_name_R2.fastq.gz"]
   #   # Add more samples as needed following this structure.
   #
   # Ensure that the 'fastq' paths are relative to 'fastq_dir' or absolute paths.
   # The workflow will automatically manage software dependencies using conda environments defined in the workflow.
   
   # Execute the Skipper workflow
   # Ensure you are in the 'skipper' directory where the Snakefile and config.yaml are located.
   # The --use-conda flag tells Snakemake to manage environments via conda.
   # The --cores flag specifies the number of CPU cores to use.
   # The --configfile flag points to your configuration file.
   snakemake --use-conda --cores 10 --configfile config.yaml

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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 available
   # conda install -c bioconda skewer
   
   # Define input and output file names
   INPUT_R1="reads_R1.fastq.gz"
   INPUT_R2="reads_R2.fastq.gz"
   OUTPUT_PREFIX="trimmed_reads"
   
   # Define common eCLIP 3' adapter sequence
   # This adapter is often used in eCLIP protocols (e.g., TruSeq Small RNA 3' adapter)
   ADAPTER_3PRIME="AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC"
   
   # Execute skewer for adapter and quality trimming
   # -x: 3' adapter sequence
   # -q: Minimum average quality score to keep a read (e.g., 20 for Phred+33)
   # -l: Minimum read length after trimming
   # -m any: Trimming mode (any means trim any adapter found)
   # -o: Output file prefix
   skewer -x "${ADAPTER_3PRIME}" -q 20 -l 18 -m any -o "${OUTPUT_PREFIX}" "${INPUT_R1}" "${INPUT_R2}"

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3. 3

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

   fastp v0.23.4 GitHub

   $ Bash example

   # Install fastp using conda
   # conda install -c bioconda fastp
   
   # Define input and output file names
   # Replace with actual input file paths for your raw sequencing reads
   INPUT_READ1="raw_sequencing_R1.fastq.gz"
   INPUT_READ2="raw_sequencing_R2.fastq.gz" # If paired-end, provide R2 file
   
   # Define output file names for reads with UMIs extracted
   OUTPUT_READ1="umi_extracted_R1.fastq.gz"
   OUTPUT_READ2="umi_extracted_R2.fastq.gz" # If paired-end, provide R2 output file
   
   # Define report file names for fastp's quality control and UMI processing summary
   REPORT_JSON="fastp_umi_report.json"
   REPORT_HTML="fastp_umi_report.html"
   
   # Execute fastp to extract UMIs from raw sequencing reads
   # This command assumes UMIs are located at the beginning of Read 1
   # and are 10 base pairs long. The UMI sequence will be moved to the read ID
   # (e.g., @read_id UMI:ATGC...).
   #
   # IMPORTANT: Adjust --umi_loc and --umi_len based on your specific library
   # preparation protocol and UMI design. Common locations include 'read1',
   # 'read2', 'index1', 'index2'.
   fastp \
       --in1 "${INPUT_READ1}" \
       --in2 "${INPUT_READ2}" \
       --out1 "${OUTPUT_READ1}" \
       --out2 "${OUTPUT_READ2}" \
       --umi_loc read1 \
       --umi_len 10 \
       --umi_prefix "UMI:" \
       --json "${REPORT_JSON}" \
       --html "${REPORT_HTML}" \
       --thread 8 # Adjust number of threads as needed for your system
   

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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 v2.5.2b GitHub

   $ Bash example

   # Install STAR if not already installed
   # conda install -c bioconda star
   
   # Example values for placeholders
   STAR_GENOME_DIR="/path/to/STAR_genome_index_hg38" # e.g., from ENCODE or custom build
   INPUT_FASTQ="sample_R1.fastq.gz"
   OUTPUT_PREFIX="sample_aligned"
   NUM_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:"${REPLICATE_LABEL}" \
        --outSAMattributes All \
        --outSAMmode Full \
        --outSAMtype BAM Unsorted \
        --outSAMunmapped Within \
        --readFilesCommand zcat \
        --outStd Log \
        --readFilesIn "${INPUT_FASTQ}" \
        --runMode alignReads \
        --runThreadN "${NUM_THREADS}"

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5. 5

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

   Skipper vlatest GitHub

   $ Bash example

   # Clone the Skipper repository
   # git clone https://github.com/yeolab/skipper.git
   # cd skipper
   
   # Create and activate the conda environment
   # conda env create -f environment.yaml
   # conda activate skipper_env
   
   # --- Placeholder for config.yaml ---
   # Create a configuration file (config.yaml) with your specific inputs and parameters.
   # This is an example; actual parameters will depend on your data and analysis goals.
   #
   # Example config.yaml content:
   #
   # samples:
   #   sample1:
   #     replicates:
   #       rep1:
   #         ip_bam: "path/to/sample1_rep1_ip.bam"
   #         input_bam: "path/to/sample1_rep1_input.bam"
   #       rep2:
   #         ip_bam: "path/to/sample1_rep2_ip.bam"
   #         input_bam: "path/to/sample1_rep2_input.bam"
   #
   # genome: "hg38" # Or mm10, etc.
   # genome_fasta: "path/to/genome.fa"
   # genome_chrom_sizes: "path/to/genome.chrom.sizes"
   # blacklist_bed: "path/to/blacklist.bed"
   # repeatmasker_bed: "path/to/repeatmasker.bed" # For repetitive elements annotation
   #
   # output_dir: "results"
   #
   # --- End of config.yaml placeholder ---
   
   # Execute the Skipper Snakemake workflow
   # This command will run the entire pipeline, including peak calling, IDR for reproducible peaks,
   # and annotation with repetitive elements, based on the configuration in config.yaml.
   snakemake --snakefile Snakefile --configfile config.yaml --cores 8 --use-conda

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Tools Used