GSE220462 Processing Pipeline

Publication

Neuron (2024) — PMID 38697111

Dataset

Epistatic interactions between NMD and TRP53 control progenitor cell maintenance and brain size

1. 1

   The raw data was mapped using STAR.

   STAR v2.7.9a (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install STAR (example using conda)
   # conda install -c bioconda star
   
   # Create STAR genome index (if not already done)
   # Replace /path/to/genome_fasta.fa with your reference genome FASTA file
   # Replace /path/to/annotations.gtf with your gene annotations GTF file
   # Replace /path/to/STAR_index_hg38 with your desired index directory
   # STAR --runThreadN 8 --runMode genomeGenerate --genomeDir /path/to/STAR_index_hg38 --genomeFastaFiles /path/to/genome_fasta.fa --sjdbGTFfile /path/to/annotations.gtf --sjdbOverhang 100
   
   # Define variables
   GENOME_DIR="/path/to/STAR_index_hg38" # Placeholder: Replace with path to your STAR genome index for hg38
   READ1="sample_R1.fastq.gz" # Placeholder: Replace with your R1 FASTQ file
   READ2="sample_R2.fastq.gz" # Placeholder: Replace with your R2 FASTQ file (remove if single-end)
   OUTPUT_PREFIX="sample_aligned_"
   THREADS=8 # Number of threads to use
   
   # Run STAR alignment
   STAR \
     --genomeDir ${GENOME_DIR} \
     --readFilesIn ${READ1} ${READ2} \
     --runThreadN ${THREADS} \
     --outFileNamePrefix ${OUTPUT_PREFIX} \
     --outSAMtype BAM SortedByCoordinate \
     --outSAMattributes All \
     --outFilterMismatchNmax 3 \
     --outFilterScoreMinOverLread 0.66 \
     --outFilterMatchNminOverLread 0.66 \
     --alignIntronMin 20 \
     --alignIntronMax 1000000 \
     --alignMatesGapMax 1000000 \
     --outReadsUnmapped Fastx \
     --outFilterType BySJout \
     --outFilterMultimapNmax 20 \
     --outFilterMultimapScoreRange 1 \
     --outFilterScoreMin 10 \
     --outFilterMatchNmin 10 \
     --limitSjdbInsertNsj 1200000 \
     --sjdbScore 1 \
     --seedSearchStartLmax 30 \
     --seedPerReadNmax 1000 \
     --seedPerWindowNmax 50 \
     --alignTranscriptsPerReadNmax 10000 \
     --alignTranscriptsPerWindowNmax 1000
   
   # The output BAM file will be named sample_aligned_Aligned.sortedByCoord.out.bam
   # Other output files (e.g., Log.final.out, SJ.out.tab) will also be generated.

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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

   # Installation of common tools that might be wrapped by an in-house script
   # conda install -c bioconda subread # For featureCounts
   # conda install -c conda-forge r-base # For R-based DE analysis (DESeq2, edgeR)
   # R -e "install.packages('DESeq2')"
   # R -e "install.packages('edgeR')"
   
   # Placeholder for input BAM files (replace with actual paths)
   BAM_FILES="sample1_rep1.bam sample1_rep2.bam sample2_rep1.bam sample2_rep2.bam"
   
   # Placeholder for gene annotation GTF file (replace with actual path or download)
   # Example download for GRCh38:
   # wget -O Homo_sapiens.GRCh38.109.gtf.gz "https://ftp.ensembl.org/pub/release-109/gtf/homo_sapiens/Homo_sapiens.GRCh38.109.gtf.gz"
   # gunzip Homo_sapiens.GRCh38.109.gtf.gz
   GENE_ANNOTATION="Homo_sapiens.GRCh38.109.gtf"
   
   # Placeholder for experimental design file (e.g., tab-separated file with sample_id, condition)
   # Example design.tsv:
   # sample_id    condition
   # sample1_rep1    treated
   # sample1_rep2    treated
   # sample2_rep1    control
   # sample2_rep2    control
   DESIGN_FILE="design.tsv"
   
   # Execute the conceptual in-house script
   # The actual command would depend on the implementation of the in-house script.
   # It typically takes aligned BAM files, a gene annotation GTF, and a design file.
   # It performs gene quantification and differential expression analysis.
   ./in_house_expression_pipeline.sh \
     --bams ${BAM_FILES} \
     --gtf ${GENE_ANNOTATION} \
     --design ${DESIGN_FILE} \
     --output_dir ./results

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Tools Used