GSE232515 Processing Pipeline

Publication

Nature communications (2024) — PMID 39152130

Dataset

Expanded repertoire for RNA-editing-based detection for RNA binding protein interactions (3)

1. 1

   remove adapter with Cutadapt

   cutadapt v4.0 (Inferred from yeolab/skipper workflow) GitHub

   $ Bash example

   # Install cutadapt (example using conda)
   # conda install -c bioconda cutadapt
   
   # Define input and output files
   INPUT_FASTQ="path/to/your/input_reads.fastq.gz"
   OUTPUT_FASTQ="path/to/your/trimmed_reads.fastq.gz"
   
   # Define adapter sequence(s)
   # For eCLIP, this sequence is typically specific to the library preparation kit.
   # Example: Illumina TruSeq RNA 3' Adapter
   ADAPTER_3PRIME="AGATCGGAAGAGCACACGTCTGAACTCCAGTCA"
   # If a 5' adapter is also present, define it here:
   # ADAPTER_5PRIME="YOUR_5_PRIME_ADAPTER_SEQUENCE"
   
   # Define trimming parameters
   MIN_LENGTH=18 # Minimum read length after trimming
   QUALITY_CUTOFF=20 # Quality cutoff for trimming low-quality bases from 3' end
   
   # Run cutadapt to remove 3' adapter
   # -a: 3' adapter sequence (for single-end or 3' end of forward read in paired-end)
   # -o: output file for trimmed reads
   # --minimum-length: discard reads shorter than MIN_LENGTH after trimming
   # --quality-cutoff: trim low-quality bases from the 3' end based on QUALITY_CUTOFF
   cutadapt -a "${ADAPTER_3PRIME}" \
            -o "${OUTPUT_FASTQ}" \
            --minimum-length "${MIN_LENGTH}" \
            --quality-cutoff "${QUALITY_CUTOFF}" \
            "${INPUT_FASTQ}"
   
   # For paired-end reads, the command would look like this:
   # INPUT_R1="path/to/your/input_R1.fastq.gz"
   # INPUT_R2="path/to/your/input_R2.fastq.gz"
   # OUTPUT_R1="path/to/your/trimmed_R1.fastq.gz"
   # OUTPUT_R2="path/to/your/trimmed_R2.fastq.gz"
   # ADAPTER_FWD="AGATCGGAAGAGCACACGTCTGAACTCCAGTCA" # Adapter for R1
   # ADAPTER_REV="AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT" # Adapter for R2 (often reverse complement or different)
   # cutadapt -a "${ADAPTER_FWD}" -A "${ADAPTER_REV}" \
   #          -o "${OUTPUT_R1}" -p "${OUTPUT_R2}" \
   #          --minimum-length "${MIN_LENGTH}" \
   #          --quality-cutoff "${QUALITY_CUTOFF}" \
   #          "${INPUT_R1}" "${INPUT_R2}"

   Copy View on GitHub
2. 2

   align to human genome using STAR 2.7.6a

   STAR v2.7.6a GitHub

   $ Bash example

   # Define variables
   FASTQ_R1="sample_R1.fastq.gz"
   FASTQ_R2="sample_R2.fastq.gz" # Remove if single-end
   GENOME_DIR="/path/to/STAR_human_GRCh38_index" # Replace with actual path to STAR genome index (e.g., GRCh38/hg38)
   OUTPUT_PREFIX="sample_aligned_"
   THREADS=8 # Adjust as needed
   
   # Installation (example, uncomment and modify if needed)
   # conda install -c bioconda star=2.7.6a
   
   # Run STAR alignment
   STAR \
     --genomeDir "${GENOME_DIR}" \
     --readFilesIn "${FASTQ_R1}" "${FASTQ_R2}" \
     --runThreadN "${THREADS}" \
     --outFileNamePrefix "${OUTPUT_PREFIX}" \
     --outSAMtype BAM SortedByCoordinate \
     --readFilesCommand zcat \
     --outSAMattributes Standard \
     --quantMode GeneCounts \
     --twopassMode Basic

   Copy View on GitHub
3. 3

   SAILOR analysis of data for C-to-U edits

   SAILOR vNot specified

   $ Bash example

   # Install Miniconda or Anaconda if not already installed
   # conda create -n sailor_env python=2.7 pysam numpy scipy
   # conda activate sailor_env
   # git clone https://github.com/gersteinlab/SAILOR.git
   # cd SAILOR
   
   # Define input and output paths
   INPUT_BAM="input.bam" # Placeholder: Path to your aligned RNA-seq BAM file
   OUTPUT_PREFIX="sailor_output" # Prefix for output files
   SAMPLE_NAME="sample1" # A name for the sample
   
   # Define reference files (using latest human assembly as placeholder if not specified)
   REFERENCE_FASTA="GRCh38.p14.genome.fa" # Placeholder: Path to reference genome FASTA
   GENE_ANNOTATION_GTF="gencode.v45.annotation.gtf" # Placeholder: Path to gene annotation GTF
   
   # Execute SAILOR for C-to-U edit detection
   # Assuming SAILOR.py is in the current directory or in PATH
   # Default parameters are used as no specific parameters were provided in the description.
   # -c: Minimum coverage (default 10)
   # -q: Minimum base quality (default 20)
   # -m: Minimum mapping quality (default 20)
   # -e: Minimum editing ratio (default 0.1)
   # -d: Minimum depth for editing site (default 5)
   python SAILOR.py \
       -i "${INPUT_BAM}" \
       -o "${OUTPUT_PREFIX}" \
       -r "${REFERENCE_FASTA}" \
       -g "${GENE_ANNOTATION_GTF}" \
       -s "${SAMPLE_NAME}" \
       -c 10 \
       -q 20 \
       -m 20 \
       -e 0.1 \
       -d 5

   Copy
4. 4

   SAILOR analysis of data for A-to-I edits

   SAILOR vv1.0.0

   $ Bash example

   # Install SAILOR (if not already installed)
   # pip install SAILOR
   
   # Define input and output paths
   INPUT_BAM="path/to/your/aligned_reads.bam" # Replace with your input BAM file
   REFERENCE_FASTA="path/to/your/reference_genome.fa" # e.g., hg38.fa, replace with your reference genome FASTA
   OUTPUT_DIR="sailor_analysis_output"
   
   # Create output directory if it doesn't exist
   mkdir -p "${OUTPUT_DIR}"
   
   # Run SAILOR for A-to-I RNA editing detection
   # Adjust parameters like --min-coverage, --min-base-quality, --min-mapping-quality as needed
   SAILOR -i "${INPUT_BAM}" -r "${REFERENCE_FASTA}" -o "${OUTPUT_DIR}"

   Copy

Tools Used