GSE86041 Processing Pipeline

Publication

Neuron (2016) — PMID 27773581

Dataset

HNRNPA2B1 regulates alternative RNA processing in the nervous system and accumulates in granules in ALS IPSC-derived motor neurons [iCLIP-seq]

1. 1

   Raw CLIP-seq reads were 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 GitHub

   $ Bash example

   # Install cutadapt (e.g., using conda)
   # conda install -c bioconda cutadapt=2.10
   
   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 \
     raw_reads.fastq.gz

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2. 2

   Trimmed reads were mapped against a database of repetitive elements derived from RepBase (version 18.05) using Bowtie (version 1.0.0) with parameters -S -q -p 16 -e 100 -l 20 (Langmead et al.

   Bowtie v1.0.0 GitHub

   $ Bash example

   # Install Bowtie (if not already installed)
   # conda install -c bioconda bowtie
   
   # Define input and output files
   # Replace 'trimmed_reads.fastq' with your actual trimmed reads file
   TRIMMED_READS="trimmed_reads.fastq"
   # Replace 'repbase_18.05' with the path to your Bowtie index for repetitive elements
   # This index should be built from the RepBase (version 18.05) repetitive elements database
   BOWTIE_INDEX="repbase_18.05"
   OUTPUT_SAM="mapped_to_repbase.sam"
   
   # Run Bowtie mapping
   # Trimmed reads were mapped against a database of repetitive elements derived from RepBase (version 18.05)
   # using Bowtie (version 1.0.0) with parameters -S -q -p 16 -e 100 -l 20
   bowtie -S -q -p 16 -e 100 -l 20 "${BOWTIE_INDEX}" "${TRIMMED_READS}" > "${OUTPUT_SAM}"

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3. 3

   2009).

   N/A (Inferred with models/gemini-2.5-flash) vN/A (Inferred with models/gemini-2.5-flash)
4. 4

   Reads not mapped to repetitive elements were mapped to the mm9 mouse genome (UCSC assembly) using STAR (version 2.3.03) with parameters --outSAMunmapped Within –outFilterMultimapNmax 1 –outFilterMultimapScoreRange 1 (Dobin et al.

   STAR v2.3.03 GitHub

   $ Bash example

   # Install STAR if not already installed
   # conda install -c bioconda star=2.3.03
   
   # Placeholder for STAR genome index directory for mm9 (UCSC assembly)
   # You would need to download or build the mm9 STAR index first.
   # Example command to build index (replace paths and threads):
   # STAR --runMode genomeGenerate --genomeDir /path/to/mm9_star_index --genomeFastaFiles /path/to/mm9.fa --sjdbGTFfile /path/to/mm9.gtf --runThreadN <num_threads>
   GENOME_DIR="/path/to/mm9_star_index" # Replace with actual path to mm9 STAR index
   
   # Placeholder for input reads file (FASTQ format, pre-filtered for repetitive elements)
   INPUT_READS="input_reads.fastq" # Replace with your actual input FASTQ file
   
   # Placeholder for output prefix
   OUTPUT_PREFIX="mapped_reads"
   
   STAR --genomeDir "${GENOME_DIR}" \
        --readFilesIn "${INPUT_READS}" \
        --outSAMunmapped Within \
        --outFilterMultimapNmax 1 \
        --outFilterMultimapScoreRange 1 \
        --outFileNamePrefix "${OUTPUT_PREFIX}" \
        --outSAMtype BAM SortedByCoordinate \
        --runThreadN 8 # Example: Adjust number of threads as needed
   

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5. 5

   2013).

   (Inferred with models/gemini-2.5-flash) vN/A

   $ Bash example

   # No specific tool or command could be inferred from the description '2013)'.
   # Please provide more context to generate a relevant bash command.
   
   # Placeholder for a generic reference genome (e.g., human hg38)
   # This would typically be used by an alignment or peak calling tool.
   # GENOME_FASTA="/path/to/your/genome/hg38.fa"
   # GENOME_INDEX_PREFIX="/path/to/your/genome/index/hg38" # For aligners like STAR, HISAT2
   
   # Placeholder for input and output files
   # INPUT_FASTQ="sample.fastq.gz"
   # OUTPUT_BAM="aligned.bam"
   # OUTPUT_PEAKS="peaks.bed"
   
   # Example of a generic command structure (replace with actual tool and parameters)
   # For alignment:
   # STAR --runThreadN 8 --genomeDir "${GENOME_INDEX_PREFIX}" --readFilesIn "${INPUT_FASTQ}" --outFileNamePrefix "${OUTPUT_BAM%.bam}"
   # For peak calling (e.g., CLIPper, MACS2):
   # clipper -i "${OUTPUT_BAM}" -o "${OUTPUT_PEAKS}" -s hg38
   
   echo "Placeholder: No specific bioinformatics command inferred due to insufficient description."

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6. 6

   Reads having the same 5’ mapping position were collapsed to a single read to eliminate PCR duplication.

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

   $ Bash example

   # Install samtools if not already installed
   # conda install -c bioconda samtools
   
   # Input BAM file (assumed to be sorted by coordinate)
   INPUT_BAM="aligned_reads.bam"
   OUTPUT_BAM="deduplicated_reads.bam"
   
   # Collapse reads having the same 5' mapping position to a single read to eliminate PCR duplication.
   # The -r option removes duplicate reads instead of just marking them.
   samtools markdup -r "${INPUT_BAM}" "${OUTPUT_BAM}"

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7. 7

   CLIP-seq peaks were identified as previously described (Zisoulis et al, NSMB 2010).

   CLIP-seq vlatest GitHub

   $ Bash example

   # Install clipper (if not already installed)
   # git clone https://github.com/yeolab/clipper.git
   # cd clipper
   # python setup.py install # Or just use the script directly
   
   # Placeholder variables - User should replace these with actual file paths
   # For human (hg38) genome, you can download .fa and .gtf from UCSC or Ensembl.
   IP_BAM="path/to/your/ip.bam"
   CONTROL_BAM="path/to/your/control.bam" # Optional, but highly recommended for CLIP-seq
   GENOME_FASTA="path/to/your/hg38.fa" 
   GENOME_ANNOTATION="path/to/your/hg38.gtf" 
   OUTPUT_DIR="clipper_peaks"
   P_VALUE=0.01
   FOLD_ENRICHMENT=2
   STRAND="." # Use '.' for unstranded, '+' for forward, '-' for reverse
   THREADS=8 # Number of CPU threads to use
   
   # Create output directory
   mkdir -p "${OUTPUT_DIR}"
   
   # Execute clipper
   python /path/to/clipper/clipper.py \
       -o "${OUTPUT_DIR}" \
       -p "${P_VALUE}" \
       -f "${FOLD_ENRICHMENT}" \
       -s "${STRAND}" \
       -g "${GENOME_FASTA}" \
       -a "${GENOME_ANNOTATION}" \
       -c "${CONTROL_BAM}" \
       -t "${THREADS}" \
       "${IP_BAM}"

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Tools Used