GSE69586 Processing Pipeline

Publication

Molecular cell (2015) — PMID 26253027

Dataset

Target discrimination in nonsense-mediated mRNA decay requires Upf1 ATPase activity

1. 1

   Sequencing reads from CLIP-seq and RIP-seq libraries were first trimmed of polyA tails, adapters, and low quality ends using cutadapt with parameters --match-read-wildcards --times 2 -e 0 -O 5 --quality-cutoff' 6 -m 18 -b TCGTATGCCGTCTTCTGCTTG -b ATCTCGTATGCCGTCTTCTGCTTG -b CGACAGGTTCAGAGTTCTACAGTCCGACGATC -b TGGAATTCTCGGGTGCCAAGG -b AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -b TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT.

   cutadapt v2.10 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install cutadapt (e.g., using conda)
   # conda install -c bioconda cutadapt
   
   # Define input and output file names
   INPUT_READS="reads.fastq.gz"
   OUTPUT_TRIMMED_READS="trimmed_reads.fastq.gz"
   
   # Run cutadapt to trim polyA tails, adapters, and low quality ends
   cutadapt \
     --match-read-wildcards \
     --times 2 \
     -e 0 \
     -O 5 \
     --quality-cutoff 6 \
     -m 18 \
     -b TCGTATGCCGTCTTCTGCTTG \
     -b ATCTCGTATGCCGTCTTCTGCTTG \
     -b CGACAGGTTCAGAGTTCTACAGTCCGACGATC \
     -b TGGAATTCTCGGGTGCCAAGG \
     -b AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA \
     -b TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT \
     -o "${OUTPUT_TRIMMED_READS}" \
     "${INPUT_READS}"

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2. 2

   Reads were then mapped against a database of repetitive elements derived from RepBase18.05.

   bowtie2 (Inferred with models/gemini-2.5-flash) v2.5.2 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install bowtie2 (if not already installed)
   # conda install -c bioconda bowtie2
   
   # --- Reference Data Preparation ---
   # RepBase18.05 is a proprietary database. Access typically requires a license.
   # The following is a placeholder for how one would prepare the index if the FASTA file was available.
   
   # Placeholder for RepBase18.05 FASTA file (replace with actual path to your downloaded file)
   # REPBASE_FASTA="path/to/RepBase18.05.fasta"
   # REPBASE_INDEX_PREFIX="reference/RepBase/RepBase18.05"
   
   # Create directory for reference if it doesn't exist
   # mkdir -p $(dirname "${REPBASE_INDEX_PREFIX}")
   
   # Build Bowtie2 index for RepBase18.05
   # bowtie2-build "${REPBASE_FASTA}" "${REPBASE_INDEX_PREFIX}"
   
   # --- Alignment Step ---
   
   # Define input and output files
   # Assuming paired-end reads, adjust for single-end if necessary
   READ1="input_read1.fastq.gz"
   READ2="input_read2.fastq.gz"
   
   # Path to the Bowtie2 index prefix for RepBase18.05
   # This should be the same prefix used during the bowtie2-build step (e.g., 'reference/RepBase/RepBase18.05')
   REPBASE_INDEX_PREFIX="reference/RepBase/RepBase18.05"
   
   OUTPUT_SAM="aligned_to_repeats.sam"
   UNALIGNED_PREFIX="unaligned_to_repeats"
   THREADS=8 # Number of threads to use
   
   # Align reads against the RepBase18.05 repetitive elements database
   # --very-sensitive-local is often used for mapping to repetitive elements to allow for local alignments
   # --un-conc-gz outputs unaligned paired reads to two gzipped files (e.g., unaligned_to_repeats.1.fastq.gz, unaligned_to_repeats.2.fastq.gz)
   bowtie2 \
     --very-sensitive-local \
     -p "${THREADS}" \
     -x "${REPBASE_INDEX_PREFIX}" \
     -1 "${READ1}" \
     -2 "${READ2}" \
     --un-conc-gz "${UNALIGNED_PREFIX}" \
     -S "${OUTPUT_SAM}"
   
   # Optional: Convert SAM to BAM and sort
   # samtools view -bS "${OUTPUT_SAM}" | samtools sort -o "${OUTPUT_SAM%.sam}.bam"
   # samtools index "${OUTPUT_SAM%.sam}.bam"

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3. 3

   Bowtie version 1.0.0 with parameters -S -q -p 16 -e 100 -l 20 was used to align reads against an index generated from Repbase sequences (Langmead et al., 2009).

   Bowtie v1.0.0 GitHub

   $ Bash example

   # Install Bowtie (if not already installed)
   # conda install -c bioconda bowtie=1.0.0
   
   # Assuming 'repbase_index' is the base name of the Bowtie index files
   # and 'reads.fastq' is the input reads file.
   # The output will be a SAM file named 'output.sam'.
   bowtie -S -q -p 16 -e 100 -l 20 repbase_index reads.fastq > output.sam

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4. 4

   Reads not mapped to Repbase sequences were aligned to the hg19 human genome (UCSC assembly) using STAR (Dobin et al., 2013) version 2.3.0e with parameters --outSAMunmapped Within –outFilterMultimapNmax 1 –outFilterMultimapScoreRange 1.

   STAR v2.3.0e GitHub

   $ Bash example

   # Install STAR (example using conda)
   # conda install -c bioconda star=2.3.0e
   
   # Create STAR genome index for hg19 if not already done
   # STAR --runMode genomeGenerate --genomeDir /path/to/hg19_STAR_index --genomeFastaFiles /path/to/hg19.fa --sjdbGTFfile /path/to/hg19.gtf --runThreadN <num_threads>
   
   # Align reads to hg19
   STAR --genomeDir /path/to/hg19_STAR_index \
        --readFilesIn unmapped_reads.fastq \
        --outSAMunmapped Within \
        --outFilterMultimapNmax 1 \
        --outFilterMultimapScoreRange 1 \
        --outFileNamePrefix star_hg19_alignment_

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5. 5

   Reads that were PCR replicates were removed from each CLIP-seq library using a custom script.

   CLIP-seq v1.1.2 GitHub

   $ Bash example

   # Install umi_tools
   # conda install -c bioconda umi_tools
   
   # Install samtools (if not already installed)
   # conda install -c bioconda samtools
   
   # Define input and output file names
   INPUT_BAM="input_aligned_reads.bam"
   OUTPUT_DEDUP_BAM="deduplicated_reads.bam"
   
   # Sort the BAM file by coordinate (required for umi_tools dedup)
   samtools sort -o "${INPUT_BAM%.bam}.sorted.bam" "$INPUT_BAM"
   
   # Index the sorted BAM file
   samtools index "${INPUT_BAM%.bam}.sorted.bam"
   
   # Remove PCR replicates using umi_tools dedup.
   # This command assumes Unique Molecular Identifiers (UMIs) are embedded in the read ID,
   # separated by a colon, which is a common format in eCLIP workflows (e.g., from the Yeo lab).
   # Adjust --extract-umi-method and --umi-separator if your UMI structure is different.
   umi_tools dedup \
       --extract-umi-method=read_id \
       --umi-separator=':' \
       -I "${INPUT_BAM%.bam}.sorted.bam" \
       -S "$OUTPUT_DEDUP_BAM"
   
   # Index the deduplicated BAM file
   samtools index "$OUTPUT_DEDUP_BAM"

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6. 6

   Briefly one read was kept at each nucleotide position when more than one read’s 5' end was mapped

   dedup_reads.py (Inferred with models/gemini-2.5-flash) vCustom Script (from Yeo Lab eCLIP pipeline) GitHub

   $ Bash example

   # Install Python (if not already available)
   # conda install python=3.8
   
   # Install pysam, a dependency for dedup_reads.py
   # pip install pysam
   
   # Clone the eclip repository to obtain the dedup_reads.py script
   # git clone https://github.com/yeolab/eclip.git
   
   # Define input and output file paths
   INPUT_BAM="aligned_reads.bam" # Placeholder for the input BAM file
   OUTPUT_DEDUP_BAM="deduplicated_reads.bam" # Placeholder for the deduplicated output BAM file
   
   # Path to the dedup_reads.py script within the cloned repository
   DEDUP_SCRIPT="eclip/scripts/dedup_reads.py"
   
   # Execute the deduplication script
   # This script identifies reads with identical 5' end mapping positions and keeps only one, effectively removing PCR duplicates based on 5' end.
   python "${DEDUP_SCRIPT}" -i "${INPUT_BAM}" -o "${OUTPUT_DEDUP_BAM}"

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7. 7

   Clusters were then assigned using the CLIPper software with parameters --bonferroni --superlocal --threshold- software (Lovci et al., 2013).

   CLIPper vInferred from Lovci et al., 2013 publication GitHub

   $ Bash example

   # Installation (example, adjust based on environment):
   # git clone https://github.com/yeolab/clipper.git
   # cd clipper
   # # Ensure Python dependencies are installed (e.g., pip install numpy scipy pysam)
   
   # Example usage of CLIPper based on description
   # Note: Input BAM file, genome size, and output file names are placeholders.
   # The '--threshold-' parameter in the description is incomplete; a placeholder [VALUE] is used.
   # Replace 'input_aligned_reads.bam', 'genome_size.txt', and '[VALUE]' with actual values relevant to your data.
   python clipper.py \
     -b input_aligned_reads.bam \
     -s genome_size.txt \
     -o clipper_peaks.bed \
     --bonferroni \
     --superlocal \
     --threshold [VALUE]

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8. 8

   conclusions discussed in the associated manuscript are based on the BAM files

   clipper (Inferred with models/gemini-2.5-flash) vunspecified GitHub

   $ Bash example

   # Clone the clipper repository
   # git clone https://github.com/yeolab/clipper.git
   # cd clipper
   
   # Example: Create a dummy genome size file for hg38
   # This file should contain chromosome names and their lengths, tab-separated.
   # For a real analysis, obtain this from a genome assembly provider (e.g., UCSC, Ensembl).
   # Example for hg38 (partial):
   # echo -e "chr1\t248956422" > hg38.chrom.sizes
   # echo -e "chr2\t242193529" >> hg38.chrom.sizes
   # echo -e "chr3\t198295559" >> hg38.chrom.sizes
   # ... add all chromosomes ...
   
   # Run clipper for peak calling
   # Replace 'input.bam' with your actual BAM file
   # Replace 'hg38.chrom.sizes' with your genome size file (e.g., for Homo sapiens, build hg38)
   # Replace 'output_peaks' with your desired output prefix
   python clipper.py -b input.bam -s hg38.chrom.sizes -o output_peaks

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Tools Used