GSE213867 Processing Pipeline

Publication

Cell genomics (2023) — PMID 37388912

Dataset

Skipper analysis of RNA-protein interactions highlights depletion of genetic variation in translation factor binding sites

1. 1

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

   Skipper vNot specified GitHub

   $ Bash example

   # Clone the Skipper repository (if not already cloned)
   # git clone https://github.com/yeolab/skipper.git
   # cd skipper
   
   # Install Snakemake and dependencies (if not already installed)
   # conda create -n skipper_env snakemake mamba -c conda-forge -c bioconda
   # conda activate skipper_env
   
   # Placeholder for configuration:
   # A 'config.yaml' file is typically required to specify input files,
   # reference genomes, and other parameters for the Skipper workflow.
   # Example content for config.yaml (refer to Skipper's documentation for details):
   # samples:
   #   sample1:
   #     R1: "path/to/sample1_R1.fastq.gz"
   #     R2: "path/to/sample1_R2.fastq.gz" # Optional for single-end
   # genome: "path/to/reference_genome.fa" # Placeholder for reference genome
   # annotation: "path/to/genome_annotation.gtf" # Placeholder for genome annotation
   # output_dir: "results"
   
   # Run the Skipper Snakemake workflow
   # Adjust --cores based on available resources.
   # Ensure 'config.yaml' is properly configured for your data and references.
   snakemake --use-conda --cores 8 --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 (Inferred from yeolab/skipper eCLIP workflow) GitHub

   $ Bash example

   # Install skewer (e.g., via conda)
   # conda create -n skewer_env skewer=0.2.2 -c bioconda -c conda-forge
   # conda activate skewer_env
   
   # Placeholder for adapter and barcode sequences. These would typically be provided in a FASTA file.
   # Example content for adapters.fa (replace with actual sequences):
   # >adapter1
   # AGATCGGAAGAGCACACGTCTGAACTCCAGTCA
   # >barcode_sequence_example
   # GATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
   
   # Define input and output file names
   INPUT_R1="input_R1.fastq.gz"
   INPUT_R2="input_R2.fastq.gz"
   OUTPUT_PREFIX="trimmed_reads"
   ADAPTER_FILE="adapters.fa"
   THREADS=8
   MIN_LENGTH=18
   MIN_QUALITY=20
   
   # Trim reads for adapter and barcode sequences using skewer
   skewer -x "${ADAPTER_FILE}" \
          -m any \
          -l "${MIN_LENGTH}" \
          -q "${MIN_QUALITY}" \
          -t "${THREADS}" \
          -o "${OUTPUT_PREFIX}" \
          "${INPUT_R1}" \
          "${INPUT_R2}"
   
   # Expected output files:
   # trimmed_reads-trimmed-pair1.fastq.gz
   # trimmed_reads-trimmed-pair2.fastq.gz
   # trimmed_reads-trimmed.log

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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) GitHub

   $ Bash example

   # Install fastp (if not already installed)
   # conda install -c bioconda fastp
   
   # Define input and output file names (placeholders)
   # Replace with your actual input and desired output file names
   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_report.json"
   REPORT_HTML="fastp_report.html"
   
   # Extract UMIs from raw sequencing reads using fastp
   # This command assumes UMIs are at the beginning of Read 1 and are 10 bp long.
   # The UMI sequence will be moved from the read sequence to the read ID, prefixed with 'UMI:'.
   # Basic quality control (trimming low-quality bases, adapter trimming, filtering short reads) is also included.
   fastp \
     --in1 "${INPUT_R1}" \
     --out1 "${OUTPUT_R1}" \
     --in2 "${INPUT_R2}" \
     --out2 "${OUTPUT_R2}" \
     --umi_loc read1 \
     --umi_len 10 \
     --umi_prefix "UMI:" \
     --qualified_quality_phred 20 \
     --unqualified_percent_limit 50 \
     --length_required 50 \
     --detect_adapter_for_pe \
     --json "${REPORT_JSON}" \
     --html "${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.x GitHub

   $ Bash example

   # Install STAR using conda
   # conda install -c bioconda star
   
   # Define variables (replace with actual paths and values)
   genome_dir="/path/to/STAR_genome_index/GRCh38"
   output_prefix="sample_aligned"
   input_fastq="sample.fastq.gz"
   num_threads=8
   replicate_id="sample1_rep1"
   
   # Run STAR alignment
   STAR \
     --alignEndsType EndToEnd \
     --genomeDir "${genome_dir}" \
     --genomeLoad NoSharedMemory \
     --outBAMcompression 10 \
     --outFileNamePrefix "${output_prefix}" \
     --winAnchorMultimapNmax 100 \
     --outFilterMultimapNmax 100 \
     --outFilterMultimapScoreRange 1 \
     --outSAMmultNmax 1 \
     --outMultimapperOrder Random \
     --outFilterScoreMin 10 \
     --outFilterType BySJout \
     --limitOutSJcollapsed 5000000 \
     --outReadsUnmapped None \
     --outSAMattrRGline ID:"${replicate_id}" \
     --outSAMattributes All \
     --outSAMmode Full \
     --outSAMtype BAM Unsorted \
     --outSAMunmapped Within \
     --readFilesCommand zcat \
     --outStd Log \
     --readFilesIn "${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 vSnakemake workflow (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Clone the Skipper repository
   # git clone https://github.com/yeolab/skipper.git
   # cd skipper
   
   # Create a Conda environment for Snakemake
   # conda create -n skipper_env snakemake -y
   # conda activate skipper_env
   
   # --- Configuration for Skipper workflow ---
   # Create a config.yaml file with your sample and genome information.
   # Example config.yaml:
   # samples:
   #   sample_rep1:
   #     r1: "/path/to/sample_rep1_R1.fastq.gz"
   #     r2: "/path/to/sample_rep1_R2.fastq.gz" # Optional, if paired-end
   #   sample_rep2:
   #     r1: "/path/to/sample_rep2_R1.fastq.gz"
   #     r2: "/path/to/sample_rep2_R2.fastq.gz" # Optional, if paired-end
   #   input_rep1: # Control/Input sample
   #     r1: "/path/to/input_rep1_R1.fastq.gz"
   #     r2: "/path/to/input_rep1_R2.fastq.gz" # Optional, if paired-end
   #   input_rep2: # Control/Input sample
   #     r1: "/path/to/input_rep2_R1.fastq.gz"
   #     r2: "/path/to/input_rep2_R2.fastq.gz" # Optional, if paired-end
   #
   # genome_assembly: "hg38" # e.g., hg38, mm10
   # genome_dir: "/path/to/STAR/index/for/hg38" # Path to STAR genome index
   # gtf_file: "/path/to/gencode.vXX.annotation.gtf" # Path to GTF annotation file
   # repeat_masker_file: "/path/to/hg38.fa.out.bed" # Path to RepeatMasker BED file
   #
   # # Ensure all necessary reference files (STAR index, GTF, RepeatMasker BED) are prepared.
   # # For hg38, these can be downloaded from ENCODE or UCSC.
   # # Example for hg38:
   # # STAR index: https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/ (e.g., star_2.7.10a_idx.tar)
   # # GTF: https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_44/gencode.v44.annotation.gtf.gz
   # # RepeatMasker BED: Can be generated from UCSC Table Browser (track: RepeatMasker, output format: BED) or downloaded from specific resources.
   
   # Execute the Skipper Snakemake workflow to perform peak calling, IDR, and annotation.
   # This command will run the 'all' rule, which includes reproducible enriched window (IDR)
   # and repetitive element annotation steps.
   snakemake --snakefile Snakefile --configfile config.yaml --cores 8 all

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Tools Used