GSE39872 Processing Pipeline

Publication

Molecular cell (2012) — PMID 22959275

Dataset

LIN28 binds messenger RNAs at GGAGA motifs and regulates splicing factor abundance (HTS)

1. 1

   Read mapping from CLIP-seq experiments and data processing was performed as published (Polymenidou et al., 2011).

   CLIP-seq v2.7.10a GitHub

   $ Bash example

   # Install STAR (if not already installed)
   # conda install -c bioconda star
   
   # --- Genome Index Preparation (Run once per genome, assuming hg38 as a placeholder) ---
   # This step generates the STAR genome index files. Replace paths and files as needed.
   # STAR --runMode genomeGenerate \
   #      --genomeDir /path/to/STAR_genome_index/hg38 \
   #      --genomeFastaFiles /path/to/hg38.fa \
   #      --sjdbGTFfile /path/to/gencode.v38.annotation.gtf \
   #      --runThreadN 16
   
   # --- Read Mapping for CLIP-seq ---
   # Define variables
   GENOME_DIR="/path/to/STAR_genome_index/hg38" # Placeholder for STAR genome index directory
   INPUT_FASTQ="input.fastq.gz" # Placeholder for input CLIP-seq FASTQ file (e.g., from a single-end experiment)
   OUTPUT_PREFIX="mapped_reads" # Prefix for output files
   THREADS=8 # Number of threads to use
   
   # Perform read mapping using STAR. Parameters are chosen to be suitable for CLIP-seq, 
   # focusing on unique mapping and minimal splicing to reflect direct RNA binding.
   STAR --genomeDir "${GENOME_DIR}" \
        --readFilesIn "${INPUT_FASTQ}" \
        --runThreadN "${THREADS}" \
        --outFileNamePrefix "${OUTPUT_PREFIX}_" \
        --outSAMtype BAM SortedByCoordinate \
        --outSAMattributes All \
        --outFilterMultimapNmax 1 \
        --outFilterMismatchNmax 3 \
        --outFilterScoreMinOverLread 0.66 \
        --outFilterMatchNminOverLread 0.66 \
        --alignIntronMax 1 \
        --alignMatesGapMax 1000000 \
        --alignSJDBoverhangMin 1 \
        --alignSJoverhangMin 8 \
        --seedSearchStartLmax 15 \
        --seedPerReadNmax 100000 \
        --seedPerWindowNmax 100 \
        --winAnchorMultimapNmax 50 \
        --outReadsUnmapped Fastx \
        --quantMode GeneCounts # Optional: for gene quantification, often useful for CLIP-seq

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2. 2

   Briefly, reads were processed and mapped to the human genome (hg18 http://genome.ucsc.edu; Bowtie version 0.12.2, with parameters -q -p 4 -e 70 -y -l 25 -n 2 -m 5 --best --strata) and assigned to 21,605 genes (as annotated previously (Yeo et al., 2009)).

   Bowtie v0.12.2 GitHub

   $ Bash example

   # Install Bowtie (version 0.12.2)
   # conda install -c bioconda bowtie=0.12.2
   
   # Download hg18 reference genome if not available
   # wget https://hgdownload.soe.ucsc.edu/goldenPath/hg18/bigZips/hg18.fa.gz
   # gunzip hg18.fa.gz
   #
   # Build Bowtie index (if not pre-built). This will create several files with the 'hg18' prefix.
   # bowtie-build hg18.fa hg18
   
   # Align reads to the hg18 human genome using Bowtie
   # Assuming 'reads.fastq' is your input FASTQ file and 'hg18' is the prefix for your Bowtie index files.
   bowtie -q -p 4 -e 70 -y -l 25 -n 2 -m 5 --best --strata hg18 reads.fastq > output.sam

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3. 3

   LIN28ES_CLIPseq_clusters.BED: hg18

   clipper vlatest GitHub

   $ Bash example

   # Install clipper (if not already installed)
   # pip install clipper
   # # Or clone from GitHub and install
   # # git clone https://github.com/yeolab/clipper.git
   # # cd clipper
   # # python setup.py install
   
   # Assuming input BAM file is LIN28ES_CLIPseq_aligned.bam
   # And clipper.py is in your PATH or specified with its full path
   clipper.py \
       --species hg18 \
       --threshold-method p_value \
       --threshold 0.05 \
       --output-file LIN28ES_CLIPseq_clusters.BED \
       LIN28ES_CLIPseq_aligned.bam

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Tools Used