GSE220460 Processing Pipeline

Publication

Neuron (2024) — PMID 38697111

Dataset

Epistatic interactions between NMD and TRP53 control progenitor cell maintenance and brain size (RNA-seq e13invivo)

1. 1

   The raw data was mapped using STAR.

   STAR v2.7.10a GitHub

   $ Bash example

   # Install STAR (if not already installed)
   # conda install -c bioconda star
   
   # Define variables (replace with actual paths and filenames)
   GENOME_DIR="/path/to/STAR_index/hg38" # Placeholder: Use a STAR-indexed human genome (e.g., hg38)
   READ1_FASTQ="input_R1.fastq.gz" # Placeholder: Path to your R1 FASTQ file
   READ2_FASTQ="input_R2.fastq.gz" # Placeholder: Path to your R2 FASTQ file (remove if single-end)
   OUTPUT_PREFIX="mapped_data" # Prefix for output files
   NUM_THREADS=8 # Number of threads to use
   
   # Create genome index if not already present (run once per genome)
   # STAR --runMode genomeGenerate \
   #      --genomeDir ${GENOME_DIR} \
   #      --genomeFastaFiles /path/to/hg38.fa \
   #      --sjdbGTFfile /path/to/gencode.vXX.annotation.gtf \
   #      --runThreadN ${NUM_THREADS}
   
   # Map raw data using STAR
   STAR --genomeDir ${GENOME_DIR} \
        --readFilesIn ${READ1_FASTQ} ${READ2_FASTQ} \
        --runThreadN ${NUM_THREADS} \
        --outFileNamePrefix ${OUTPUT_PREFIX}_ \
        --outSAMtype BAM SortedByCoordinate \
        --outFilterMultimapNmax 20 \
        --alignSJoverhangMin 8 \
        --outFilterMismatchNmax 3 \
        --outFilterScoreMinOverLread 0.66 \
        --outFilterMatchNminOverLread 0.66 \
        --quantMode GeneCounts # Optional: Add GeneCounts for gene expression quantification
   

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2. 2

   We calculated the gene-level read counts and identified differentially expressed genes by in-house script.

   In-house script vCustom

   $ Bash example

   # This script represents a conceptual execution of an "in-house script"
   # for calculating gene-level read counts and performing differential expression analysis.
   # The actual script name, programming language (e.g., Python, R), and parameters
   # would be specific to the in-house implementation.
   
   # --- Reference Data Setup (Example: Ensembl GRCh38, release 111 GTF) ---
   # Download the gene annotation file if not already present.
   # mkdir -p references
   # cd references
   # wget -c https://ftp.ensembl.org/pub/release-111/gtf/homo_sapiens/Homo_sapiens.GRCh38.111.gtf.gz
   # gunzip -f Homo_sapiens.GRCh38.111.gtf.gz
   # cd ..
   
   GENE_ANNOTATION="references/Homo_sapiens.GRCh38.111.gtf" # Path to your GTF file
   
   # --- Input Data (Example: Aligned BAM files) ---
   # These are placeholder BAM files that would typically be generated in a preceding alignment step.
   # Replace with actual paths to your input BAM files.
   INPUT_BAM_FILES=(
       "data/sample_treated_rep1.bam"
       "data/sample_treated_rep2.bam"
       "data/sample_control_rep1.bam"
       "data/sample_control_rep2.bam"
   )
   
   # Convert array to space-separated string for command line
   INPUT_BAM_STRING="${INPUT_BAM_FILES[*]}"
   
   # --- Experimental Design File ---
   # A design file (e.g., CSV or TSV) is crucial for differential expression analysis,
   # mapping samples to experimental conditions.
   # Example content for 'design.csv':
   # sample_id,condition
   # sample_treated_rep1,treated
   # sample_treated_rep2,treated
   # sample_control_rep1,control
   # sample_control_rep2,control
   #
   # Create a placeholder design file if it doesn't exist
   # echo "sample_id,condition" > design.csv
   # echo "sample_treated_rep1,treated" >> design.csv
   # echo "sample_treated_rep2,treated" >> design.csv
   # echo "sample_control_rep1,control" >> design.csv
   # echo "sample_control_rep2,control" >> design.csv
   
   DESIGN_FILE="design.csv"
   
   # --- Output Files ---
   OUTPUT_COUNTS_FILE="gene_level_read_counts.tsv"
   OUTPUT_DE_RESULTS="differentially_expressed_genes.tsv"
   OUTPUT_LOG="in_house_script.log"
   
   # --- Execute the In-House Script ---
   # This command is a conceptual representation.
   # The actual script name and parameters would vary based on the in-house implementation.
   # It is assumed this script handles both gene counting and DE analysis.
   in_house_gene_quant_and_de_script.py \
       --input_bams "${INPUT_BAM_STRING}" \
       --gene_annotation "${GENE_ANNOTATION}" \
       --design_file "${DESIGN_FILE}" \
       --output_counts "${OUTPUT_COUNTS_FILE}" \
       --output_de_results "${OUTPUT_DE_RESULTS}" \
       --log_file "${OUTPUT_LOG}"

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Tools Used