GSE284636 Processing Pipeline

Publication

Nature microbiology (2025) — PMID 41093992

Dataset

Short 5’ UTRs serve as a marker for mRNA translation inhibition by the IFIT2-IFIT3 antiviral complex

1. 1

   Standard processing of eCLIP data was performed as previously described (Blue SB, et al.

   eCLIP vCWL workflow (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install cwltool if not already installed
   # pip install cwltool
   
   # Define placeholder paths for input files and reference genome
   # Replace these with your actual file paths and ensure reference files are indexed/prepared as required by the workflow.
   # For human (hg38) reference, ensure you have the FASTA, GTF, STAR index, chromosome sizes, and blacklist regions.
   FASTQ_R1="/path/to/your/sample_R1.fastq.gz"
   FASTQ_R2="/path/to/your/sample_R2.fastq.gz" # Set to "null" if single-end, e.g., FASTQ_R2="null"
   GENOME_FASTA="/path/to/your/reference/hg38.fa"
   GENOME_GTF="/path/to/your/reference/hg38.gtf"
   STAR_INDEX_DIR="/path/to/your/reference/STAR_index_hg38"
   CHROM_SIZES="/path/to/your/reference/hg38.chrom.sizes"
   BLACKLIST_BED="/path/to/your/reference/hg38_blacklist.bed"
   OUTPUT_DIR="./eclip_output"
   
   # Create output directory if it doesn't exist
   mkdir -p "${OUTPUT_DIR}"
   
   # Create a CWL input YAML file for the eCLIP workflow
   # Note: If FASTQ_R2 is "null", the entry for fastq_r2 in the YAML should be 'fastq_r2: null'
   cat << EOF > eclip_inputs.yaml
   fastq_r1:
     class: File
     path: ${FASTQ_R1}
   fastq_r2:
     class: File
     path: ${FASTQ_R2}
   genome_fasta:
     class: File
     path: ${GENOME_FASTA}
   genome_gtf:
     class: File
     path: ${GENOME_GTF}
   star_index:
     class: Directory
     path: ${STAR_INDEX_DIR}
   chrom_sizes:
     class: File
     path: ${CHROM_SIZES}
   blacklist_regions:
     class: File
     path: ${BLACKLIST_BED}
   output_dir: ${OUTPUT_DIR}
   EOF
   
   # Execute the eCLIP CWL workflow using cwltool
   # Replace '/path/to/yeolab/eclip/workflow.cwl' with the actual path to the workflow.cwl file
   # from the cloned yeolab/eclip repository.
   cwltool --outdir "${OUTPUT_DIR}" /path/to/yeolab/eclip/workflow.cwl eclip_inputs.yaml
   

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2. 2

   Nature Protocols 2022), with mapping performed to a custom genome index that included both the VSV genome and either hg19 (for 293T experiments) or mm10 (for MEF experiments) as described below.

   STAR (Inferred with models/gemini-2.5-flash) v2.7.10a GitHub

   $ Bash example

   # Install STAR (if not already installed)
   # conda install -c bioconda star
   
   # Define variables for input and output
   # CUSTOM_GENOME_INDEX_DIR should point to the pre-built STAR index
   # This index would have been created from a combination of the VSV genome and either hg19 (for 293T experiments) or mm10 (for MEF experiments).
   # Example for a 293T experiment (adjust paths and filenames as needed):
   CUSTOM_GENOME_INDEX_DIR="/path/to/star_index_vsv_hg19" # Placeholder for the custom genome index directory (e.g., VSV+hg19)
   READ1_FASTQ="sample_R1.fastq.gz" # Placeholder for input Read 1 FASTQ file
   READ2_FASTQ="sample_R2.fastq.gz" # Placeholder for input Read 2 FASTQ file (remove if single-end reads)
   OUTPUT_PREFIX="aligned_sample" # Prefix for output files
   NUM_THREADS=8 # Number of threads to use for alignment
   
   # Perform alignment using STAR
   # --genomeDir: Path to the STAR genome index
   # --readFilesIn: Input FASTQ files (space-separated for paired-end, single file for single-end)
   # --runThreadN: Number of threads for parallel processing
   # --outFileNamePrefix: Prefix for all output files generated by STAR
   # --outSAMtype BAM SortedByCoordinate: Output a sorted BAM file
   # --readFilesCommand zcat: Command to decompress gzipped FASTQ files on-the-fly
   STAR --genomeDir "${CUSTOM_GENOME_INDEX_DIR}" \
        --readFilesIn "${READ1_FASTQ}" "${READ2_FASTQ}" \
        --runThreadN "${NUM_THREADS}" \
        --outFileNamePrefix "${OUTPUT_PREFIX}_" \
        --outSAMtype BAM SortedByCoordinate \
        --readFilesCommand zcat
   

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3. 3

   UMI removal and cut adapt: umi_tools extract --random-seed 1 --bc-pattern NNNNNNNNNN --stdin 23145FL-08-01-37_S37_L001_R1_001.fastq.gz --log /dev/null | cutadapt -j 8 --match-read-wildcards --times 1 -e 0.1 --quality-cutoff 6 -m 18 -a a_adapters.fasta -O 1 - --report minimal 2> EV225_IP.cut.adapt.log | cutadapt -j 8 --match-read-wildcards --times 1 -e 0.1 --quality-cutoff 6 -m 18 -a a_adapters.fasta -O 5 - -o EV225_IP.trim.gz --report minimal >>EV225_IP.cut.adapt.log

   cutadapt v4.0 GitHub

   $ Bash example

   # Reference dataset 'a_adapters.fasta' source is not specified.
   umi_tools extract --random-seed 1 --bc-pattern NNNNNNNNNN --stdin 23145FL-08-01-37_S37_L001_R1_001.fastq.gz --log /dev/null | \
   cutadapt -j 8 --match-read-wildcards --times 1 -e 0.1 --quality-cutoff 6 -m 18 -a a_adapters.fasta -O 1 - --report minimal 2> EV225_IP.cut.adapt.log | \
   cutadapt -j 8 --match-read-wildcards --times 1 -e 0.1 --quality-cutoff 6 -m 18 -a a_adapters.fasta -O 5 - -o EV225_IP.trim.gz --report minimal >>EV225_IP.cut.adapt.log

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4. 4

   Repeat mapping (takes adapter-trimmed fastq files, and maps to a repeat database to filter repeat elements prior to genome mapping: STAR --runMode alignReads --runThreadN 8 --alignEndsType EndToEnd --genomeDir [mus_musculus_repbase_v2 or homo_sapiens_repbase_v2] --genomeLoad NoSharedMemory --outBAMcompression 10 --outFileNamePrefix EV225_IP.repeat.map --outFilterMultimapNmax 100 --outFilterMultimapScoreRange 1 --outFilterScoreMin 10 --outFilterType BySJout --outReadsUnmapped Fastx --outSAMattrRGline ID:foo --outSAMattributes All --outSAMmode Full --outSAMtype BAM Unsorted --outSAMunmapped None --outStd Log --readFilesCommand zcat --readFilesIn EV225_IP.trim.gz

   STAR vInferred with models/gemini-2.5-flash GitHub

   $ Bash example

   # Install STAR (if not already installed)
   # conda install -c bioconda star
   
   # Define variables
   INPUT_FASTQ="EV225_IP.trim.gz"
   OUTPUT_PREFIX="EV225_IP.repeat.map"
   # Choose the appropriate repeat database genome directory:
   # For Mus musculus: GENOME_DIR="mus_musculus_repbase_v2"
   # For Homo sapiens: GENOME_DIR="homo_sapiens_repbase_v2"
   GENOME_DIR="[mus_musculus_repbase_v2 or homo_sapiens_repbase_v2]" # Placeholder - replace with actual path
   THREADS=8
   
   # Execute STAR command for repeat mapping
   STAR --runMode alignReads \
        --runThreadN "${THREADS}" \
        --alignEndsType EndToEnd \
        --genomeDir "${GENOME_DIR}" \
        --genomeLoad NoSharedMemory \
        --outBAMcompression 10 \
        --outFileNamePrefix "${OUTPUT_PREFIX}" \
        --outFilterMultimapNmax 100 \
        --outFilterMultimapScoreRange 1 \
        --outFilterScoreMin 10 \
        --outFilterType BySJout \
        --outReadsUnmapped Fastx \
        --outSAMattrRGline ID:foo \
        --outSAMattributes All \
        --outSAMmode Full \
        --outSAMtype BAM Unsorted \
        --outSAMunmapped None \
        --outStd Log \
        --readFilesCommand zcat \
        --readFilesIn "${INPUT_FASTQ}"

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5. 5

   Unique genomic mapping (takes repeat-removed reads and maps to the reference genome: STAR --runMode alignReads --runThreadN 8 --alignEndsType EndToEnd --genomeDir [hg19+VSV or mm10+VSV] --genomeLoad NoSharedMemory --outBAMcompression 10 --outFileNamePrefix EV225_IP.genome.map --outFilterMultimapNmax 1 --outFilterMultimapScoreRange 1 --outFilterScoreMin 10 --outFilterType BySJout --outReadsUnmapped Fastx --outSAMattrRGline ID:foo --outSAMattributes All --outSAMmode Full --outSAMtype BAM Unsorted --outSAMunmapped None --outStd Log --readFilesIn EV225_IP.repeat.mapUnmapped.out.mate1

   STAR v2.7.9a GitHub

   $ Bash example

   # Install STAR (example using conda)
   # conda install -c bioconda star
   
   # Placeholder for genome directory.
   # This directory should contain the STAR-indexed genome (e.g., hg19 with VSV sequences).
   # Example for genome generation:
   # STAR --runMode genomeGenerate --genomeDir hg19_vsv_genome_dir --genomeFastaFiles hg19.fa vsv.fa --sjdbGTFfile annotation.gtf --runThreadN 8
   GENOME_DIR="hg19_vsv_genome_dir" # Replace with the actual path to your indexed genome (e.g., hg19+VSV or mm10+VSV)
   
   # Input reads file
   READS_FILE="EV225_IP.repeat.mapUnmapped.out.mate1"
   
   # Output prefix
   OUTPUT_PREFIX="EV225_IP.genome.map"
   
   STAR \
     --runMode alignReads \
     --runThreadN 8 \
     --alignEndsType EndToEnd \
     --genomeDir "${GENOME_DIR}" \
     --genomeLoad NoSharedMemory \
     --outBAMcompression 10 \
     --outFileNamePrefix "${OUTPUT_PREFIX}" \
     --outFilterMultimapNmax 1 \
     --outFilterMultimapScoreRange 1 \
     --outFilterScoreMin 10 \
     --outFilterType BySJout \
     --outReadsUnmapped Fastx \
     --outSAMattrRGline ID:foo \
     --outSAMattributes All \
     --outSAMmode Full \
     --outSAMtype BAM Unsorted \
     --outSAMunmapped None \
     --outStd Log \
     --readFilesIn "${READS_FILE}"

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6. 6

   Sort mapped reads: samtools sort -@ 8 -m 2G -o EV225_IP.genome.map.bam *Aligned.out.bam

   samtools v1.9 GitHub

   $ Bash example

   # Install samtools if not already available
   # conda install -c bioconda samtools
   
   # Define input and output file paths
   # Replace 'sample_name_Aligned.out.bam' with your actual input BAM file.
   # The description uses '*Aligned.out.bam' as a placeholder for the input file.
   INPUT_BAM="sample_name_Aligned.out.bam"
   OUTPUT_BAM="EV225_IP.genome.map.bam"
   
   # Sort mapped reads
   samtools sort -@ 8 -m 2G -o "${OUTPUT_BAM}" "${INPUT_BAM}"

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7. 7

   Remove PCR duplicate reads: samtools index EV225_IP.genome.map.bam umi_tools dedup --random-seed 1 --stdin EV225_IP.genome.map.bam --method unique --stdout EV225_IP.bam

   UMI-tools vNot specified (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   samtools index EV225_IP.genome.map.bam
   umi_tools dedup --random-seed 1 --stdin EV225_IP.genome.map.bam --method unique --stdout EV225_IP.bam

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8. 8

   Make bigwigs: samtools index EV225_IP.bam b2bw EV225_IP.bam --cpus 1

   samtools v3.5.1 GitHub

   $ Bash example

   # Install samtools
   # conda install -c bioconda samtools
   
   # Install deepTools
   # conda install -c bioconda deepTools
   
   # Index the BAM file, a prerequisite for bigWig generation
   samtools index EV225_IP.bam
   
   # Generate bigWig file from the indexed BAM file
   # The description uses 'b2bw', which is inferred to be a custom script or alias wrapping deepTools bamCoverage.
   # We use common parameters for bigWig generation from sequencing data.
   bamCoverage -b EV225_IP.bam \
               -o EV225_IP.bigwig \
               --numberOfProcessors 1 \
               --binSize 10 \
               --normalizeUsing RPKM

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9. 9

   Isolate VSV-mapped reads only: bigWigToWig EV225.pos.bw -chrom=chrVSV EV225.pos.bw.chrVSV.wig

   bigWigToWig vUCSC Genome Browser utilities (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install UCSC Genome Browser utilities if not already installed
   # conda install -c bioconda ucsc-bigwigtowig
   
   bigWigToWig EV225.pos.bw -chrom=chrVSV EV225.pos.bw.chrVSV.wig

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10. 10

    wigToBigWig EV225.pos.bw.chrVSV.wig /path/to/hg19_and_vsv.chrom.sizes EV242.pos.bw.chrVSV.wig.bw

    UCSC tools vUCSC tools GitHub

    $ Bash example

    # Install UCSC tools if not already available
    # conda install -c bioconda ucsc-wigtobigwig
    
    # Define input, reference, and output files
    INPUT_WIG="EV225.pos.bw.chrVSV.wig"
    CHROM_SIZES="/path/to/hg19_and_vsv.chrom.sizes" # This file should contain chromosome names and their sizes for hg19 and VSV. A standard hg19.chrom.sizes can be obtained from UCSC, but 'hg19_and_vsv' implies a custom file.
    OUTPUT_BIGWIG="EV242.pos.bw.chrVSV.wig.bw"
    
    # Execute the wigToBigWig command
    wigToBigWig "${INPUT_WIG}" "${CHROM_SIZES}" "${OUTPUT_BIGWIG}"

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Tools Used