GSE220459 Processing Pipeline

Publication

Neuron (2024) — PMID 38697111

Dataset

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

1. 1

   The raw data was mapped using STAR.

   STAR v2.7.10a (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install STAR (example using conda)
   # conda create -n star_env star -y
   # conda activate star_env
   
   # Build STAR genome index (run once per genome, e.g., GRCh38)
   # mkdir -p /path/to/genome_index/GRCh38
   # STAR --runThreadN 8 \
   #      --runMode genomeGenerate \
   #      --genomeDir /path/to/genome_index/GRCh38 \
   #      --genomeFastaFiles /path/to/fasta/GRCh38.primary_assembly.genome.fa \
   #      --sjdbGTFfile /path/to/gtf/gencode.v38.annotation.gtf \
   #      --sjdbOverhang 100 # Recommended: (read_length - 1)
   
   # Map raw data using STAR (example for paired-end reads)
   STAR --runThreadN 8 \
        --genomeDir /path/to/genome_index/GRCh38 \
        --readFilesIn sample_R1.fastq.gz sample_R2.fastq.gz \
        --readFilesCommand zcat \
        --outFileNamePrefix sample_ \
        --outSAMtype BAM SortedByCoordinate \
        --outBAMcompression 6 \
        --outFilterMultimapNmax 20 \
        --outFilterMismatchNmax 3 \
        --alignIntronMax 1000000 \
        --alignMatesGapMax 1000000 \
        --quantMode GeneCounts # Optional: for gene-level 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 vN/A

   $ Bash example

   # This script calculates gene-level read counts and identifies differentially expressed genes.
   # The specific implementation details are within the "in-house script".
   #
   # Pre-requisite: Gene-level read counts (e.g., from featureCounts, HTSeq-count, or RSEM).
   # Example for generating counts (assuming aligned BAM files in 'aligned_bams/'):
   # # conda install -c bioconda subread
   # # featureCounts -a gencode.v44.annotation.gtf -o gene_counts.tsv -F GTF -t exon -g gene_id aligned_bams/*.bam
   #
   # Input for DE analysis: gene_counts.tsv (gene-level read counts), sample_metadata.tsv (experimental design)
   # Output: diff_exp_results.tsv (table of differentially expressed genes)
   #
   # Reference datasets (placeholders):
   # - Gene annotation: gencode.v44.annotation.gtf (GENCODE human release 44, GRCh38)
   
   # Execute the in-house script for differential expression analysis.
   # The actual command, script name, and parameters will depend on the specific in-house implementation.
   # This is a placeholder command demonstrating typical inputs and outputs.
   Rscript run_inhouse_de_script.R \
     --counts_file gene_counts.tsv \
     --metadata_file sample_metadata.tsv \
     --output_file diff_exp_results.tsv \
     --design_formula "~ condition + batch" \
     --min_reads_per_gene 10 \
     --fdr_threshold 0.05 \
     --log2fc_threshold 1.0

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Tools Used