GSE69583 Processing Pipeline

Publication

Nature (2016) — PMID 27121842

Dataset

Musashi-2 Post-transcriptionally Attenuates Aryl Hydrocarbon Receptor Signaling to Expand Human Hematopoietic Stem Cells

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 (e.g., using conda)
   # conda install -c bioconda cutadapt
   
   # Define input and output file names (placeholders)
   INPUT_FASTQ="input.fastq.gz"
   OUTPUT_FASTQ="trimmed.fastq.gz"
   
   # Define adapter sequences
   ADAPTER1="TCGTATGCCGTCTTCTGCTTG"
   ADAPTER2="ATCTCGTATGCCGTCTTCTGCTTG"
   ADAPTER3="CGACAGGTTCAGAGTTCTACAGTCCGACGATC"
   ADAPTER4="TGGAATTCTCGGGTGCCAAGG"
   ADAPTER_POLYA="AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
   ADAPTER_POLYT="TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT"
   
   # Execute cutadapt command
   cutadapt \
     --match-read-wildcards \
     --times 2 \
     -e 0 \
     -O 5 \
     --quality-cutoff 6 \
     -m 18 \
     -b "${ADAPTER1}" \
     -b "${ADAPTER2}" \
     -b "${ADAPTER3}" \
     -b "${ADAPTER4}" \
     -b "${ADAPTER_POLYA}" \
     -b "${ADAPTER_POLYT}" \
     -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.

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

   $ Bash example

   # Install bowtie2 (if not already installed)
   # conda install -c bioconda bowtie2
   # conda install -c bioconda samtools
   
   # --- Reference Data Preparation ---
   # Download RepBase18.05 (placeholder - actual download method may vary, often requires license)
   # For demonstration, let's assume RepBase18.05.fasta is available.
   # Example: wget "http://www.girinst.org/repbase/update/RepBase18.05.fasta.gz"
   # gunzip RepBase18.05.fasta.gz
   
   # Build the bowtie2 index for repetitive elements
   # This step needs to be done once for the reference database
   # Replace RepBase18.05.fasta with the actual path to your RepBase FASTA file
   # The index prefix will be RepBase18.05_index
   bowtie2-build RepBase18.05.fasta RepBase18.05_index
   
   # --- Read Mapping ---
   # Define input and output files
   READS_FASTQ="input_reads.fastq.gz" # Replace with your actual input reads file (can be .fastq or .fastq.gz)
   OUTPUT_SAM="mapped_to_repeats.sam"
   OUTPUT_BAM="mapped_to_repeats.bam"
   OUTPUT_SORTED_BAM="mapped_to_repeats.sorted.bam"
   INDEX_PREFIX="RepBase18.05_index"
   NUM_THREADS=8 # Adjust based on available CPU cores
   
   # Map reads against the repetitive elements database using bowtie2
   # --very-sensitive: Use a very sensitive alignment mode, good for finding matches in repetitive regions.
   # -U: For single-end reads. Use -1 <R1.fastq> -2 <R2.fastq> for paired-end reads.
   # --no-unal: Suppress SAM records for unaligned reads.
   # -S: Output SAM file.
   bowtie2 --very-sensitive -p "${NUM_THREADS}" -x "${INDEX_PREFIX}" -U "${READS_FASTQ}" -S "${OUTPUT_SAM}"
   
   # Convert SAM to BAM and sort the BAM file
   # -bS: Output in BAM format, input is SAM.
   # -o: Output file.
   samtools view -bS "${OUTPUT_SAM}" -o "${OUTPUT_BAM}"
   
   # Sort the BAM file by coordinate
   samtools sort "${OUTPUT_BAM}" -o "${OUTPUT_SORTED_BAM}"
   
   # Remove intermediate files if desired
   # rm "${OUTPUT_SAM}"
   # rm "${OUTPUT_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
   
   # Align reads using Bowtie
   # Assuming 'repbase_index' is the prefix for the Bowtie index files generated from Repbase sequences
   # Assuming 'reads.fastq' is the input FASTQ file containing the reads
   # 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 (example using conda)
   # conda install -c bioconda star=2.3.0e
   
   # Placeholder for STAR genome index generation (run once for hg19)
   # STAR --runMode genomeGenerate --genomeDir hg19_star_index --genomeFastaFiles hg19.fa --sjdbGTFfile genes.gtf --runThreadN 8
   
   # Align reads to hg19 using STAR
   STAR \
     --genomeDir hg19_star_index \
     --readFilesIn input_reads_filtered_from_repbase.fastq \
     --outFileNamePrefix star_hg19_alignment_ \
     --outSAMunmapped Within \
     --outFilterMultimapNmax 1 \
     --outFilterMultimapScoreRange 1 \
     --outSAMtype BAM SortedByCoordinate \
     --runThreadN 8

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5. 5

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

   CLIP-seq v2.23.4 GitHub

   $ Bash example

   # Install Picard (if not already installed)
   # conda install -c bioconda picard
   
   # Run Picard MarkDuplicates to remove PCR replicates
   # Assuming input.bam is a coordinate-sorted BAM file
   java -jar /path/to/picard.jar MarkDuplicates \
       INPUT=input.bam \
       OUTPUT=output_dedup.bam \
       METRICS_FILE=deduplication_metrics.txt \
       REMOVE_DUPLICATES=true \
       ASSUME_SORTED=true \
       VALIDATION_STRINGENCY=SILENT

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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) vv1.0.0 GitHub

   $ Bash example

   # Install samtools and pysam if not available
   # conda install -c bioconda samtools pysam
   
   # Clone the eCLIP workflow repository to get the script
   # git clone https://github.com/yeolab/eclip.git
   
   # Define input and output file names
   INPUT_BAM="aligned.bam"
   OUTPUT_DEDUP_BAM="deduplicated.bam"
   SORTED_BAM="aligned.sorted.bam"
   
   # Sort the input BAM file by coordinate, which is required by the deduplication script
   samtools sort -o "${SORTED_BAM}" "${INPUT_BAM}"
   
   # Index the sorted BAM file (optional, but good practice for downstream tools)
   samtools index "${SORTED_BAM}"
   
   # Execute the deduplication script
   # The script is located in the 'tools' directory of the cloned eclip repository
   python eclip/tools/dedup_reads.py "${SORTED_BAM}" "${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 v1.0 GitHub

   $ Bash example

   # Installation (example, adjust path as needed)
   # git clone https://github.com/yeolab/clipper.git
   # cd clipper
   # Ensure Python dependencies are met (e.g., numpy, scipy, pysam)
   # pip install numpy scipy pysam
   
   # Example: Run CLIPper for peak calling
   # Input: Aligned BAM file (e.g., from STAR or HISAT2)
   # Output: BED file containing identified peaks/clusters
   
   # Note: The description provided "--threshold-" which is incomplete.
   # The standard parameter is "--threshold <float>" (default: 0.05).
   # Using the default value for demonstration.
   python clipper.py \
       --bonferroni \
       --superlocal \
       --threshold 0.05 \
       input_aligned.bam \
       -o output_peaks.bed

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Tools Used