GSE232514 Processing Pipeline

Publication

Nature communications (2024) — PMID 39152130

Dataset

Expanded repertoire of RNA-editing-based detection for RNA binding protein interactions (2)

1. 1

   remove adapter with Cutadapt

   cutadapt v4.0 GitHub

   $ Bash example

   # Install Cutadapt (if not already installed)
   # conda install -c bioconda cutadapt=4.0
   
   # Define input and output file paths (placeholders)
   INPUT_READ1="input_R1.fastq.gz"
   INPUT_READ2="input_R2.fastq.gz"
   OUTPUT_READ1="trimmed_R1.fastq.gz"
   OUTPUT_READ2="trimmed_R2.fastq.gz"
   REPORT_FILE="cutadapt_report.txt"
   
   # Define adapter sequences (common Illumina adapters for eCLIP, adjust as needed based on library prep)
   # For single-end reads, only -a is needed. For paired-end, -a for R1 and -A for R2.
   ADAPTER_R1="AGATCGGAAGAGCACACGTCTGAACTCCAGTCA" # Example: Illumina Universal Adapter
   ADAPTER_R2="AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT" # Example: Illumina Small RNA 3' Adapter (often used for R2 in some protocols)
   
   # Define trimming parameters
   MIN_LENGTH=15 # Minimum read length after trimming
   QUALITY_CUTOFF=20 # Quality cutoff for 3' end trimming
   THREADS=8 # Number of CPU cores to use
   
   # Execute Cutadapt for paired-end reads
   cutadapt \
     --cores "${THREADS}" \
     -a "${ADAPTER_R1}" \
     -A "${ADAPTER_R2}" \
     -o "${OUTPUT_READ1}" \
     -p "${OUTPUT_READ2}" \
     -m "${MIN_LENGTH}" \
     -q "${QUALITY_CUTOFF}" \
     "${INPUT_READ1}" \
     "${INPUT_READ2}" \
     > "${REPORT_FILE}" 2>&1

   Copy View on GitHub
2. 2

   align to human genome using STAR 2.7.6a

   STAR v2.7.6a GitHub

   $ Bash example

   # Install STAR using conda
   # conda create -n star_env star=2.7.6a -c bioconda -c conda-forge
   # conda activate star_env
   
   # Define variables (replace with actual paths and filenames)
   # Reference genome: Human GRCh38 (hg38)
   # Source: GENCODE (for GTF) and UCSC/ENA/NCBI (for FASTA)
   GENOME_DIR="/path/to/STAR_human_GRCh38_index" # Path to pre-built STAR genome index for human GRCh38
   GTF_FILE="/path/to/gencode.v44.annotation.gtf.gz" # Path to human GRCh38 GTF file (e.g., GENCODE v44)
   READ1="sample_R1.fastq.gz"
   READ2="sample_R2.fastq.gz" # Remove this line if reads are single-end
   OUTPUT_PREFIX="sample_aligned"
   THREADS=8 # Number of threads to use
   
   # Example command to generate STAR genome index (run once per genome/GTF combination)
   # STAR --runMode genomeGenerate \
   #      --genomeDir ${GENOME_DIR} \
   #      --genomeFastaFiles /path/to/human_GRCh38.fa \
   #      --sjdbGTFfile ${GTF_FILE} \
   #      --sjdbOverhang 100 \
   #      --runThreadN ${THREADS}
   
   # Align reads to the human genome (GRCh38)
   STAR --genomeDir ${GENOME_DIR} \
        --readFilesIn ${READ1} ${READ2} \
        --readFilesCommand zcat \
        --runThreadN ${THREADS} \
        --outFileNamePrefix ${OUTPUT_PREFIX}_ \
        --outSAMtype BAM SortedByCoordinate \
        --outSAMattributes Standard \
        --outFilterType BySJout \
        --outFilterMultimapNmax 20 \
        --outFilterMismatchNmax 999 \
        --outFilterMismatchNoverLmax 0.04 \
        --alignIntronMin 20 \
        --alignIntronMax 1000000 \
        --alignMatesGapMax 1000000 \
        --sjdbGTFfile ${GTF_FILE} \
        --sjdbOverhang 100

   Copy View on GitHub
3. 3

   align unmapped reads from above to 12X MS2 stem-loop reporter mRNA using STAR 2.7.6a

   STAR v2.7.6a GitHub

   $ Bash example

   # Install STAR if not already installed
   # conda install -c bioconda star=2.7.6a
   
   # Placeholder for input unmapped reads (from a previous step)
   # This file would contain reads that did not map to a primary genome.
   UNMAPPED_READS="unmapped_reads.fastq.gz"
   
   # Placeholder for STAR index directory for the 12X MS2 stem-loop reporter mRNA.
   # This index needs to be pre-built using STAR --runMode genomeGenerate with the MS2 reporter sequence.
   MS2_REPORTER_STAR_INDEX="ms2_reporter_star_index"
   
   # Placeholder for output prefix
   OUTPUT_PREFIX="ms2_aligned_reads"
   
   # Align unmapped reads to the 12X MS2 stem-loop reporter mRNA
   STAR \
       --genomeDir "${MS2_REPORTER_STAR_INDEX}" \
       --readFilesIn "${UNMAPPED_READS}" \
       --readFilesCommand zcat \
       --outFileNamePrefix "${OUTPUT_PREFIX}" \
       --outSAMtype BAM SortedByCoordinate \
       --outSAMattributes Standard \
       --outFilterMultimapNmax 1 \
       --outFilterMismatchNmax 3 \
       --runThreadN 8 # Adjust thread count as needed
   

   Copy View on GitHub
4. 4

   SAILOR analysis of data for C-to-U edits

   SAILOR v1.0

   $ Bash example

   # Install SAILOR via conda
   # conda create -n sailor_env python=3.8
   # conda activate sailor_env
   # conda install -c bioconda sailor
   
   # Define input and output paths
   INPUT_BAM="input.bam" # Replace with your input RNA-seq BAM file
   OUTPUT_PREFIX="sailor_output" # Prefix for output files
   REFERENCE_FASTA="/path/to/GRCh38.primary_assembly.genome.fa" # Placeholder: Path to GRCh38 reference FASTA
   GENE_ANNOTATION_GTF="/path/to/gencode.v38.annotation.gtf" # Placeholder: Path to GENCODE v38 gene annotation GTF
   
   # Run SAILOR for C-to-U editing analysis
   # This command is a basic example. Refer to SAILOR documentation for more options.
   sailor -i "${INPUT_BAM}" \
          -o "${OUTPUT_PREFIX}" \
          -r "${REFERENCE_FASTA}" \
          -g "${GENE_ANNOTATION_GTF}" \
          --strand-specific # Example option: use if your RNA-seq library is strand-specific

   Copy
5. 5

   SAILOR analysis of data for A-to-I edits

   SAILOR vv1.0

   $ Bash example

   # Clone the SAILOR repository
   # git clone https://github.com/gersteinlab/SAILOR.git
   # cd SAILOR
   
   # Create a conda environment and install dependencies (pysam, numpy, scipy, matplotlib)
   # conda create -n sailor_env python=3.8 -y
   # conda activate sailor_env
   # pip install pysam numpy scipy matplotlib
   
   # Define input/output files and reference data
   # Replace 'path/to/your_aligned_reads.bam' with the actual path to your input BAM file
   INPUT_BAM="path/to/your_aligned_reads.bam"
   OUTPUT_PREFIX="sailor_output"
   
   # Reference genome and gene annotation (using GRCh38 as the latest assembly placeholder)
   # Download from sources like GENCODE, Ensembl, or UCSC
   REFERENCE_FASTA="path/to/GRCh38.primary_assembly.genome.fa"
   GENE_ANNOTATION_GTF="path/to/gencode.v38.annotation.gtf"
   
   # Strand information: 0 for unstranded, 1 for first-strand, 2 for second-strand
   # Assuming first-strand for typical RNA-seq data, adjust if your data is different
   STRAND_INFO="1"
   
   # Run SAILOR analysis for A-to-I edits
   # Ensure you are in the directory where SAILOR.py is located or provide its full path
   python SAILOR.py -i "${INPUT_BAM}" -o "${OUTPUT_PREFIX}" -r "${REFERENCE_FASTA}" -g "${GENE_ANNOTATION_GTF}" -s "${STRAND_INFO}"

   Copy

Tools Used