GSE69584 Processing Pipeline

Publication

Molecular cell (2015) — PMID 26253027

Dataset

Target discrimination in nonsense-mediated mRNA decay requires Upf1 ATPase activity (CLIP-Seq)

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 v1.18 GitHub

   $ Bash example

   # Install cutadapt if not already installed
   # conda install -c bioconda cutadapt
   
   # Define input and output file names (placeholders)
   INPUT_FASTQ="input.fastq.gz"
   OUTPUT_FASTQ="output.fastq.gz"
   
   # Run cutadapt to trim adapters, polyA/T tails, 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 "${OUTPUT_FASTQ}" \
     "${INPUT_FASTQ}"

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2. 2

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

   bowtie (Inferred with models/gemini-2.5-flash) v1.1.2 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install bowtie and samtools if not already available
   # conda install -c bioconda bowtie samtools
   
   # Placeholder for RepBase 18.05 FASTA file. In a real pipeline, this would be downloaded or pre-existing.
   # For example, you might download from GIRI or a local mirror.
   # wget -O RepBase18.05.fasta "http://www.girinst.org/repbase/update/RepBase18.05.fasta.gz" # (Example, actual URL may vary or require license)
   # gunzip RepBase18.05.fasta.gz
   
   # Build the bowtie index for RepBase 18.05
   bowtie-build RepBase18.05.fasta RepBase18.05_index
   
   # Map reads against the RepBase 18.05 index
   # -S: output SAM format
   # -v 2: allow up to 2 mismatches
   # -m 1: suppress alignments that are not unique (map to only one location)
   # --best --strata: report best alignments
   # --chunkmbs 1024: memory chunk size for indexing
   # --threads 8: use 8 threads for mapping
   # input_reads.fastq: Placeholder for the input FASTQ file (e.g., adapter-trimmed reads)
   # repbase_mapped.bam: Output BAM file containing reads mapped to repetitive elements
   bowtie -S -v 2 -m 1 --best --strata --chunkmbs 1024 --threads 8 RepBase18.05_index input_reads.fastq | samtools view -bS - > repbase_mapped.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 1.0.0 (example using conda)
   # conda install -c bioconda bowtie=1.0.0
   
   # Assuming 'repbase_index' is the path to the Bowtie index generated from Repbase sequences
   # Assuming 'reads.fastq' is the input reads file
   # Assuming 'output.sam' is the desired output SAM file
   
   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 version 2.3.0e (example using conda)
   # conda create -n star_env star=2.3.0e
   # conda activate star_env
   
   # Align reads to hg19
   # Replace /path/to/STAR_index/hg19 with the actual path to your hg19 STAR genome index.
   # Replace input_reads.fastq with the actual path to your input FASTQ file.
   # Replace output_prefix_ with your desired output file prefix.
   STAR --genomeDir /path/to/STAR_index/hg19 \
        --readFilesIn input_reads.fastq \
        --outFileNamePrefix output_prefix_ \
        --outSAMunmapped Within \
        --outFilterMultimapNmax 1 \
        --outFilterMultimapScoreRange 1

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5. 5

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

   CLIP-seq v1.19 GitHub

   $ Bash example

   # Install samtools (if not already installed)
   # conda install -c bioconda samtools
   
   # Define input and output file names
   INPUT_BAM="aligned_sorted.bam" # Replace with your actual input aligned and sorted BAM file
   OUTPUT_BAM="deduplicated.bam"
   METRICS_FILE="deduplication_metrics.txt"
   
   # Remove PCR replicates using samtools markdup
   # -r: Remove duplicate reads (rather than just marking them)
   # -s: Output statistics to stderr (redirected to METRICS_FILE)
   samtools markdup -r -s "${INPUT_BAM}" "${OUTPUT_BAM}" 2> "${METRICS_FILE}"

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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

   sambamba markdup (Inferred with models/gemini-2.5-flash) v0.7.1 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install sambamba (example using conda)
   # conda install -c bioconda sambamba
   
   # Deduplicate reads based on 5' end mapping position
   # -r: Remove duplicate reads
   # -t: Number of threads (example: 8)
   sambamba markdup -r -t 8 input.bam 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 vVersion described in Lovci et al., 2013 publication GitHub

   $ Bash example

   # Install CLIPper (example, adjust as needed)
   # CLIPper is a Python script. Ensure Python and required libraries (e.g., pysam) are installed.
   # You can clone the repository and run the script directly:
   # git clone https://github.com/yeolab/clipper.git
   # cd clipper
   
   # Example execution of CLIPper for peak assignment
   # Replace 'clip_reads.bam' with your CLIP-seq alignment file (BAM format).
   # Replace 'control_reads.bam' with your control alignment file (BAM format), if applicable.
   # The '--threshold' parameter was truncated in the description; a common p-value threshold of 0.05 is used here as an inference.
   python CLIPper.py \
       --bonferroni \
       --superlocal \
       --threshold 0.05 \
       -o clipper_peaks.bed \
       clip_reads.bam \
       control_reads.bam

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8. 8

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

   samtools (Inferred with models/gemini-2.5-flash) v1.19 GitHub

   $ Bash example

   # Install samtools if not already available
   # conda install -c bioconda samtools
   
   # Generate alignment statistics from the BAM file.
   # These statistics provide a summary of the alignment quality and read counts,
   # which are often used to inform conclusions about the sequencing data.
   # Replace 'input.bam' with the actual path to your BAM file.
   # Replace 'output_dir' with your desired output directory.
   samtools flagstat input.bam > output_dir/input.bam.flagstat.txt

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Tools Used