GSE69585 Processing Pipeline

Publication

Molecular cell (2015) — PMID 26253027

Dataset

Target discrimination in nonsense-mediated mRNA decay requires Upf1 ATPase activity (RIP-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 v1.16 GitHub

   $ Bash example

   # Install cutadapt
   # conda install -c bioconda cutadapt=1.16
   
   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 trimmed_reads.fastq.gz \
     input_reads.fastq.gz

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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) v1.2.3 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install bowtie
   # conda install -c bioconda bowtie=1.2.3
   
   # --- Reference Data Preparation (RepBase18.05) ---
   # RepBase access typically requires registration. The following is a placeholder for downloading and indexing.
   # Replace with actual download method if you have access to RepBase18.05 sequences.
   # Example: Download RepBase sequences (e.g., from girinst.org after registration)
   # wget -O RepBase18.05.fasta.gz "https://www.girinst.org/repbase/update/RepBase18.05.fasta.gz" # Placeholder URL
   # gunzip RepBase18.05.fasta.gz
   
   # Build bowtie index for RepBase18.05
   # bowtie-build RepBase18.05.fasta RepBase18.05_index
   
   # --- Mapping Reads to Repetitive Elements ---
   # Input: reads.fastq.gz (replace with your actual input reads file)
   # Output: reads_rep_mapped.sam (SAM file containing reads mapped to repetitive elements)
   # Parameters:
   #   -v 2: Allow up to 2 mismatches in the alignment.
   #   -m 1: Suppress alignments that are not unique (i.e., if a read maps to more than 1 location, it's suppressed).
   #         This is a common parameter for filtering out highly repetitive reads that map to many places.
   #   --best --strata: Report alignments in "best" strata first, then "next best", etc.
   #   -S: Output in SAM format.
   #   RepBase18.05_index: The prefix for the RepBase bowtie index.
   #   reads.fastq.gz: The input FASTQ file.
   
   bowtie -v 2 -m 1 --best --strata -S reads_rep_mapped.sam RepBase18.05_index reads.fastq.gz

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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 prefix for the Bowtie index files generated from Repbase sequences
   # Assuming 'reads.fastq' is the input reads file
   # Assuming 'aligned.sam' is the desired output SAM file
   
   bowtie -S -q -p 16 -e 100 -l 20 repbase_index reads.fastq > aligned.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
   
   # Placeholder for STAR genome index for hg19
   # This index needs to be generated once using STAR --runMode genomeGenerate
   # Example command to generate index (replace paths and thread count as needed):
   # STAR --runMode genomeGenerate \
   #   --genomeDir /path/to/hg19_star_index \
   #   --genomeFastaFiles /path/to/hg19.fa \
   #   --sjdbGTFfile /path/to/hg19_genes.gtf \
   #   --runThreadN 8
   
   # Align reads to the hg19 human genome
   STAR \
     --genomeDir /path/to/hg19_star_index \
     --readFilesIn input_reads_not_mapped_to_repbase.fastq \
     --outFileNamePrefix aligned_to_hg19_ \
     --outSAMtype BAM SortedByCoordinate \
     --outSAMunmapped Within \
     --outFilterMultimapNmax 1 \
     --outFilterMultimapScoreRange 1 \
     --runThreadN 8 # Example: use 8 threads for alignment

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5. 5

   RPKMs for each gene annotated in gencode v17 were calculated from RIP-seq data using custom scripts

   GENCODE vN/A GitHub

   $ Bash example

   # Download GENCODE v17 annotation
   # mkdir -p references
   # wget -P references ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_17/gencode.v17.annotation.gtf.gz
   # gunzip references/gencode.v17.annotation.gtf.gz
   
   # Assume aligned RIP-seq BAM file is available: rip_seq_aligned.bam
   # And the GTF file is: references/gencode.v17.annotation.gtf
   
   # 1. Count reads per gene using featureCounts from the Subread package
   # conda install -c bioconda subread
   featureCounts -a references/gencode.v17.annotation.gtf -o gene_counts.txt -F GTF -t exon -g gene_id rip_seq_aligned.bam
   
   # 2. Get total mapped reads from the BAM file (excluding unmapped reads)
   # conda install -c bioconda samtools
   total_mapped_reads=$(samtools view -c -F 4 rip_seq_aligned.bam)
   
   # 3. Calculate RPKM for each gene using a custom awk script
   # This script parses the featureCounts output and applies the RPKM formula.
   # It assumes the featureCounts output file 'gene_counts.txt' has the following columns:
   # Geneid, Chr, Start, End, Strand, Length, /path/to/rip_seq_aligned.bam (read counts)
   # It skips the initial header lines and the first data header line.
   awk -v total_reads="$total_mapped_reads" '
   BEGIN { OFS="\t"; print "Geneid", "RPKM" }
   /^#/ { next } # Skip lines starting with # (comments from featureCounts)
   NR > 1 { # Skip the first header line of the data table
       gene_id = $1;
       gene_length = $6;
       read_count = $7; # Assuming the BAM file is the 7th column in featureCounts output
       if (gene_length > 0 && total_reads > 0) {
           rpkm = (read_count * 10^9) / (gene_length * total_reads);
           print gene_id, rpkm;
       } else {
           print gene_id, 0; # Handle cases with zero length or zero total reads
       }
   }' gene_counts.txt > gene_rpkm.txt

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Tools Used