GSE50178 Processing Pipeline

Publication

Genome biology (2024) — PMID 38807229

Dataset

Identification of FUS RNA targets in HeLa cells

1. 1

   Library strategy: CLIP-Seq

   CLIP-seq vv1.0.0 GitHub

   $ Bash example

   # This script demonstrates how to execute the eCLIP CWL workflow, which processes raw sequencing data from CLIP-Seq experiments through alignment, peak calling, and other downstream analyses.
   
   # Prerequisites:
   # 1. cwltool installed (e.g., pip install cwltool)
   # 2. The eclip CWL workflow repository cloned (e.g., git clone https://github.com/yeolab/eclip.git)
   # 3. An input YAML file (e.g., eclip_inputs.yaml) defining all necessary parameters
   #    and paths to input files (FASTQ, genome reference, etc.).
   #    Example placeholder for eclip_inputs.yaml:
   #    fastq_replicate1: { class: File, path: "path/to/sample_rep1.fastq.gz" }
   #    fastq_replicate2: { class: File, path: "path/to/sample_rep2.fastq.gz" }
   #    fastq_control1: { class: File, path: "path/to/control_rep1.fastq.gz" }
   #    fastq_control2: { class: File, path: "path/to/control_rep2.fastq.gz" }
   #    genome_fasta: { class: File, path: "path/to/hg38.fa" } # Placeholder: latest human assembly
   #    genome_gtf: { class: File, path: "path/to/hg38.gtf" }
   #    rbp_name: "ExampleRBP"
   #    output_prefix: "ExampleRBP_eCLIP"
   #    # ... other parameters as required by eclip.cwl
   
   # Navigate to the cloned eclip workflow directory (if not already there)
   # cd eclip
   
   # Execute the eCLIP CWL workflow
   # Replace 'eclip_inputs.yaml' with the actual path to your input configuration file.
   cwltool --outdir ./eclip_output eclip.cwl eclip_inputs.yaml

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2. 2

   Illumina default workflow (Casava1.4 software) was used for sequence basecalling.

   Casava v1.4 GitHub

   $ Bash example

   # Casava 1.4 is a proprietary software suite integrated with Illumina sequencers for basecalling and demultiplexing.
   # It processes raw intensity data (BCL files) generated by the sequencer into FASTQ files.
   # Direct command-line execution for Casava 1.4 as a standalone tool is not typically performed by users.
   # The following is a conceptual representation of the basecalling step using Casava 1.4.
   
   # Assuming BCL files are located in a run directory and output FASTQ files are desired.
   # The actual execution is handled by the Illumina instrument control software.
   # This placeholder command represents the basecalling process.
   run_illumina_casava_basecalling \
     --version "1.4" \
     --input_bcl_directory "/path/to/illumina_run/Data/Intensities/BaseCalls" \
     --output_fastq_directory "/path/to/output/fastqs" \
     --workflow "default"

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3. 3

   CLIP-seq reads were trimmed to remove apator sequences.

   CLIP-seq v1.18 GitHub

   $ Bash example

   # Install cutadapt (e.g., using conda)
   # conda install -c bioconda cutadapt=1.18
   
   # Define input and output files, and the adapter sequence
   INPUT_READS="input_reads.fastq.gz"
   OUTPUT_READS="trimmed_reads.fastq.gz"
   ADAPTER_SEQUENCE="AGATCGGAAGAGCACACGTCTGAACTCCAGTCA" # Example adapter sequence, replace with actual adapter
   
   # Trim adapter sequences from CLIP-seq reads
   cutadapt -a "${ADAPTER_SEQUENCE}" -o "${OUTPUT_READS}" "${INPUT_READS}"

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4. 4

   Reads with identical sequence were collapsed into a signle read.

   fastx_collapser (Inferred with models/gemini-2.5-flash) v0.0.14 GitHub

   $ Bash example

   # Install FASTX-Toolkit (if not already installed)
   # conda install -c bioconda fastx_toolkit
   
   # Define input and output file paths
   input_fastq="input_reads.fastq" # Placeholder for your input FASTQ file
   output_fasta="collapsed_reads.fasta" # Placeholder for your output FASTA file
   
   # Collapse identical reads into a single read
   # -v: verbose output
   # -i: input file
   # -o: output file
   fastx_collapser -v -i "${input_fastq}" -o "${output_fasta}"

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5. 5

   Reads were aligned to the GRCh37 genome assembly using BlastN, with default parameters.

   BLAST v2.10.1

   $ Bash example

   # Install BLAST+ (if not already installed)
   # conda install -c bioconda blast
   
   # Download GRCh37 genome assembly (Ensembl release 75) as a placeholder
   # wget -O GRCh37.fa.gz "http://ftp.ensembl.org/pub/release-75/fasta/homo_sapiens/dna/Homo_sapiens.GRCh37.75.dna.primary_assembly.fa.gz"
   # gunzip GRCh37.fa.gz
   
   # Create a BLAST database for GRCh37
   # makeblastdb -in GRCh37.fa -dbtype nucl -out GRCh37_blastdb
   
   # Align reads to GRCh37 using blastn with default parameters
   # Assuming 'reads.fastq' contains the input reads and 'GRCh37_blastdb' is the pre-built database
   blastn -query reads.fastq -db GRCh37_blastdb -out blastn_alignment.txt

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6. 6

   Peaks were determined using CisGenome V2 with the following parameters: Window Size W=150 bp; Cutoff C>=12 reads; Step Size S=25 bp; Max Gap=50 bp; Min Peak Length=100 bp.

   CisGenome vV2 GitHub

   $ Bash example

   # CisGenome V2 is typically a Perl script or a compiled binary.
   # Installation might involve downloading from SourceForge and ensuring Perl dependencies are met.
   # For example:
   # wget https://sourceforge.net/projects/cisgenome/files/CisGenome_V2.0/CisGenome_V2.0.tar.gz
   # tar -xzf CisGenome_V2.0.tar.gz
   # cd CisGenome_V2.0
   # export PATH=$(pwd):$PATH
   
   # Placeholder for input BAM file (e.g., from alignment)
   INPUT_BAM="treatment.bam"
   
   # Placeholder for output peak file prefix
   OUTPUT_PREFIX="cisgenome_peaks"
   
   # Run CisGenome V2 peak detection
   # Note: The exact command might vary slightly based on the specific CisGenome V2 distribution.
   # This command assumes a 'cisgenome.pl' script is in the PATH and uses common parameter names.
   # CisGenome often works with BED files as input, so a conversion from BAM might be needed if not directly supported.
   # For simplicity, assuming it can take BAM or a pre-converted BED.
   
   cisgenome.pl peak_detection \
     -w 150 \
     -c 12 \
     -s 25 \
     -g 50 \
     -l 100 \
     ${INPUT_BAM} \
     ${OUTPUT_PREFIX}
   

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Tools Used