GSE214108 Processing Pipeline

Publication

Nature neuroscience (2023) — PMID 36510111

Dataset

RNA-Targeting CRISPR/Cas13d System Alleviates Disease-Related Phenotypes in Pre-clinical Models of Huntington’s Disease (Human).

1. 1

   RNAseq reads were adapter-trimmed using Cutadapt (v1.14) and mapped to human-specific repetitive elements from RepBase (version 18.05) by STAR (v2.4.0i) (Dobin et al., 2013).

   STAR v2.4.0i GitHub

   $ Bash example

   # Install STAR (if not already installed)
   # conda install -c bioconda star
   
   # Define variables
   NUM_THREADS=8 # Example number of threads
   INPUT_READS="trimmed_rnaseq_reads.fastq.gz" # Placeholder for adapter-trimmed RNAseq reads
   OUTPUT_DIR="star_repbase_alignment"
   REPBASE_STAR_INDEX="repbase_18.05_star_index" # Placeholder for the STAR genome index built from RepBase v18.05
   
   # Create output directory
   mkdir -p "${OUTPUT_DIR}"
   
   # Run STAR to map RNAseq reads to human-specific repetitive elements from RepBase
   STAR \
     --runThreadN "${NUM_THREADS}" \
     --genomeDir "${REPBASE_STAR_INDEX}" \
     --readFilesIn "${INPUT_READS}" \
     --outFileNamePrefix "${OUTPUT_DIR}/" \
     --outSAMtype BAM SortedByCoordinate \
     --outReadsUnmapped Fastx # Optional: to output unmapped reads
   

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2. 2

   Repeat-mapping reads were removed, and remaining reads were mapped to the human genome assembly (hg19) with STAR

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

   $ Bash example

   # Install STAR (if not already installed)
   # conda install -c bioconda star=2.7.10a
   
   # --- Reference Data Setup (Example Paths) ---
   # Download human genome assembly hg19 FASTA
   # wget -P /path/to/genome/hg19/ ftp://hgdownload.soe.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz
   # gunzip /path/to/genome/hg19/hg19.fa.gz
   
   # Download Gencode v19 GTF annotation for hg19 (recommended for RNA-seq)
   # wget -P /path/to/genome/hg19/ ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_19/gencode.v19.annotation.gtf.gz
   # gunzip /path/to/genome/hg19/gencode.v19.annotation.gtf.gz
   
   # Create STAR genome index (if not already created)
   # Replace /path/to/star_index/hg19 with your desired index directory
   # Replace /path/to/genome/hg19/hg19.fa with your FASTA file path
   # Replace /path/to/genome/hg19/gencode.v19.annotation.gtf with your GTF file path
   # --sjdbOverhang 100 is a common value for read lengths around 100bp. Adjust if your reads are different.
   # STAR --runMode genomeGenerate \
   #      --genomeDir /path/to/star_index/hg19 \
   #      --genomeFastaFiles /path/to/genome/hg19/hg19.fa \
   #      --sjdbGTFfile /path/to/genome/hg19/gencode.v19.annotation.gtf \
   #      --sjdbOverhang 100 \
   #      --runThreadN 8
   
   # --- Alignment Command ---
   # Assumes input_reads_R1.fastq.gz and input_reads_R2.fastq.gz are the paired-end reads
   # after repeat-mapping reads have been removed (or STAR will filter them with --outFilterMultimapNmax 1).
   # Replace /path/to/star_index/hg19 with the actual path to your STAR genome index.
   # Adjust --runThreadN based on available CPU cores.
   # Adjust --limitBAMsortRAM based on available RAM (e.g., 30GB for 30000000000 bytes).
   STAR --genomeDir /path/to/star_index/hg19 \
        --readFilesIn input_reads_R1.fastq.gz input_reads_R2.fastq.gz \
        --readFilesCommand zcat \
        --runThreadN 8 \
        --outFileNamePrefix aligned_reads_ \
        --outSAMtype BAM SortedByCoordinate \
        --outSAMattributes Standard \
        --outFilterMultimapNmax 1 \
        --outFilterType BySJout \
        --outFilterMismatchNmax 999 \
        --outFilterMismatchNoverLmax 0.04 \
        --alignIntronMin 20 \
        --alignIntronMax 1000000 \
        --alignMatesGapMax 1000000 \
        --limitBAMsortRAM 30000000000

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3. 3

   Read counts for all genes annotated in GENCODE (hg19) were calculated using the read summarization program featureCounts (Liao et al., 2014).

   featureCounts v1.14.0 (Inferred from publication year)

   $ Bash example

   # Install featureCounts (part of Rsubread package)
   # conda install -c bioconda rsubread
   
   # Download GENCODE hg19 annotation GTF file (release 19 corresponds to hg19)
   # Note: This is a large file and may take some time to download.
   # wget ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_19/gencode.v19.annotation.gtf.gz
   # gunzip gencode.v19.annotation.gtf.gz
   
   # Placeholder for input BAM file (replace with your actual aligned BAM file)
   INPUT_BAM="aligned_reads.bam"
   
   # Output file for gene counts
   OUTPUT_COUNTS="gene_counts.txt"
   
   # GENCODE hg19 annotation file
   GENCODE_GTF="gencode.v19.annotation.gtf"
   
   # Run featureCounts to calculate read counts for all genes
   # -a: Annotation file (GTF/GFF format)
   # -o: Output file for read counts
   # -F GTF: Specify annotation file format as GTF
   # -t exon: Summarize reads mapping to 'exon' features (default for gene counting)
   # -g gene_id: Group features by 'gene_id' attribute to count reads per gene (default)
   # -s 0: Unstranded library (default, assuming no strandedness specified in description)
   #       Use -s 1 for stranded forward, -s 2 for stranded reverse
   featureCounts \
     -a "${GENCODE_GTF}" \
     -o "${OUTPUT_COUNTS}" \
     -F GTF \
     -t exon \
     -g gene_id \
     -s 0 \
     "${INPUT_BAM}"
   

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Tools Used