GSE232520 Processing Pipeline

Publication

Nature communications (2024) — PMID 39152130

Dataset

Expanded palette of RNA base editors for comprehensive RBP-RNA interactome studies

1. 1

   remove adapter with Cutadapt

   cutadapt v4.0 GitHub

   $ Bash example

   # Install cutadapt (if not already installed)
   # conda install -c bioconda cutadapt
   
   # Example for paired-end reads, common in eCLIP assays.
   # Replace 'input_R1.fastq.gz' and 'input_R2.fastq.gz' with your actual input files.
   # Replace adapter sequences if different from standard Illumina adapters.
   # -a: Adapter sequence for read 1
   # -A: Adapter sequence for read 2
   # -o: Output file for trimmed read 1
   # -p: Output file for trimmed read 2
   # -m: Minimum read length after trimming (e.g., 18 bp)
   # -q: Trim low-quality ends from reads (e.g., quality score 20)
   # --cores: Number of CPU cores to use
   # --report=full: Print a full report of the trimming process
   
   cutadapt -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCA \
            -A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT \
            -o trimmed_R1.fastq.gz \
            -p trimmed_R2.fastq.gz \
            -m 18 \
            -q 20 \
            --cores=4 \
            --report=full \
            input_R1.fastq.gz \
            input_R2.fastq.gz

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2. 2

   align to genome using bowtie2 or bwa-mem

   Bowtie2 v2.5.2 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install Bowtie2 and Samtools (if not already installed)
   # conda install -c bioconda bowtie2 samtools
   
   # Define variables
   # Replace 'path/to/GRCh38_index' with the actual path to your pre-built Bowtie2 genome index for GRCh38.
   # You can build an index using `bowtie2-build <genome.fa> GRCh38_index`.
   GENOME_INDEX="path/to/GRCh38_index" 
   READ1="sample_R1.fastq.gz" # Replace with your R1 fastq file
   READ2="sample_R2.fastq.gz" # Replace with your R2 fastq file
   OUTPUT_SAM="sample_aligned.sam"
   OUTPUT_BAM="sample_aligned.bam"
   SORTED_BAM="sample_aligned.sorted.bam"
   
   # Align paired-end reads to the genome using Bowtie2
   # -x: specify the genome index basename
   # -1: specify the first mate reads file
   # -2: specify the second mate reads file
   # -S: specify the output SAM file
   # --threads: number of threads to use (adjust based on available CPU cores)
   bowtie2 -x "${GENOME_INDEX}" -1 "${READ1}" -2 "${READ2}" -S "${OUTPUT_SAM}" --threads 8
   
   # Convert SAM to BAM format and sort the BAM file by coordinate
   # samtools view: convert SAM to BAM (-bS) and output to file (-o)
   # samtools sort: sort BAM file by coordinate and output to file (-o)
   # -@: number of threads for samtools
   samtools view -bS "${OUTPUT_SAM}" -o "${OUTPUT_BAM}" -@ 8
   samtools sort "${OUTPUT_BAM}" -o "${SORTED_BAM}" -@ 8
   
   # Index the sorted BAM file (optional, but highly recommended for downstream tools like IGV or peak callers)
   samtools index "${SORTED_BAM}"
   
   # Clean up intermediate files (optional)
   # rm "${OUTPUT_SAM}" "${OUTPUT_BAM}"

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3. 3

   generate count tables along reporter sequence using Pysamstats

   Pysamstats v1.2.0 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install Pysamstats (if not already installed)
   # pip install pysamstats
   # or
   # conda install -c bioconda pysamstats
   
   # Generate count tables along reporter sequences
   # This command assumes you have an aligned BAM file, a BED file defining the reporter sequences,
   # and a reference FASTA file (optional, but good practice for coverage calculations).
   # The --field reads_all counts all reads overlapping the region.
   # Adjust --type and --field as needed for specific counting requirements (e.g., base_counts, reads_pp).
   pysamstats --type coverage \
              --field reads_all \
              --regions-file reporter_sequences.bed \
              --fasta reference.fa \
              input.bam > output_reporter_counts.tsv

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Tools Used