GSE277161 Processing Pipeline

Publication

Molecular cell (2024) — PMID 39303722

Dataset

Integrated multi-omics analysis of zinc finger proteins uncovers roles in RNA regulation [Ribo-STAMP cell lines]

1. 1

   We aligned reads to the human genome version GRCh38 with annotation version Gencode v40 using STAR (v2.7.1a).

   STAR v2.7.1a GitHub

   $ Bash example

   # Install STAR (e.g., using Bioconda)
   # conda install -c bioconda star=2.7.1a
   
   # --- Reference Data Setup ---
   # The human genome GRCh38 and Gencode v40 annotation are required to build the STAR index.
   # Example commands to download and build the index (run once):
   # mkdir -p /path/to/STAR_genome_index_GRCh38_Gencode_v40
   # cd /path/to/STAR_genome_index_GRCh38_Gencode_v40
   # wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_40/GRCh38.primary_assembly.genome.fa.gz
   # wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_40/gencode.v40.annotation.gtf.gz
   # gunzip GRCh38.primary_assembly.genome.fa.gz
   # gunzip gencode.v40.annotation.gtf.gz
   #
   # STAR --runThreadN 8 --runMode genomeGenerate \
   #      --genomeDir /path/to/STAR_genome_index_GRCh38_Gencode_v40 \
   #      --genomeFastaFiles GRCh38.primary_assembly.genome.fa \
   #      --sjdbGTFfile gencode.v40.annotation.gtf \
   #      --sjdbOverhang 100 # Adjust sjdbOverhang based on your read length - 1
   
   # --- Alignment Step ---
   # Define input files and genome directory
   INPUT_READS_R1="sample_R1.fastq.gz" # Replace with your actual R1 FASTQ file
   INPUT_READS_R2="sample_R2.fastq.gz" # Replace with your actual R2 FASTQ file (if paired-end)
   GENOME_INDEX_DIR="/path/to/STAR_genome_index_GRCh38_Gencode_v40" # Path to your pre-built STAR index
   OUTPUT_PREFIX="aligned_reads_"
   NUM_THREADS=8 # Number of threads to use
   
   # Align reads to the human genome (GRCh38 with Gencode v40 annotation)
   STAR --runThreadN ${NUM_THREADS} \
        --genomeDir ${GENOME_INDEX_DIR} \
        --readFilesIn ${INPUT_READS_R1} ${INPUT_READS_R2} \
        --readFilesCommand zcat \
        --outFileNamePrefix ${OUTPUT_PREFIX} \
        --outSAMtype BAM SortedByCoordinate \
        --outBAMcompression 6 \
        --limitBAMsortRAM 30000000000 # Adjust based on available RAM (e.g., 30GB)
   

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2. 2

   Bam files were then filtered to include only read1 values using samtools (v1.16) with option “view -hbf 64.”

   samtools v1.16 GitHub

   $ Bash example

   # Placeholder for input BAM file
   # input.bam: The original BAM file to be filtered.
   INPUT_BAM="input.bam"
   
   # Placeholder for output BAM file
   # read1_filtered.bam: The output BAM file containing only read1 values.
   OUTPUT_BAM="read1_filtered.bam"
   
   samtools view -hbf 64 "${INPUT_BAM}" > "${OUTPUT_BAM}"

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3. 3

   C-to-U edit sites were obtained using SAILOR.

   SAILOR v1.0

   $ Bash example

   # Clone the SAILOR repository
   # git clone https://github.com/gersteinlab/sailor.git
   # cd sailor
   
   # Install dependencies (assuming Python 3 and required libraries like pysam, numpy, scipy)
   # pip install pysam numpy scipy
   
   # Example usage: Detect C-to-U RNA editing sites
   # Replace 'aligned_reads.bam' with your actual input BAM file (e.g., from STAR or HISAT2 alignment)
   # Replace 'hg38.fa' with your reference genome FASTA file (e.g., from UCSC or Ensembl)
   # Replace 'c_to_u_edits' with your desired output prefix
   python sailor.py -i aligned_reads.bam -r hg38.fa -o c_to_u_edits

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4. 4

   Edits were divided by the featurecounts (v1.5.2) output for each gene’s exons to generate EPR values based on GENCODE v40 annotations.

   featureCounts v1.5.2 GitHub

   $ Bash example

   # Install featureCounts (part of Subread package)
   # conda install -c bioconda subread
   
   # Define variables
   # Placeholder for GENCODE v40 annotations. Replace with the actual path to your GTF file.
   ANNOTATION_GTF="/path/to/gencode.v40.annotation.gtf"
   # Placeholder for the input BAM file containing aligned reads.
   INPUT_BAM="input_aligned_reads.bam"
   # Output file for gene exon counts.
   OUTPUT_FILE="gene_exon_counts.txt"
   
   # Execute featureCounts to count reads over exons for each gene.
   # -a: Specify the annotation file (GTF/GFF).
   # -o: Specify the output file for counts.
   # -F GTF: Specify that the annotation file is in GTF format.
   # -t exon: Count features of type 'exon'.
   # -g gene_id: Aggregate counts by 'gene_id' (i.e., sum exon counts for each gene).
   # Note: Strandedness (-s 0/1/2) is not specified in the description. Adjust if your data is stranded.
   featureCounts -a "${ANNOTATION_GTF}" -o "${OUTPUT_FILE}" -F GTF -t exon -g gene_id "${INPUT_BAM}"

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Tools Used