GSE214110 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

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
   READS_FILE="trimmed_rnaseq_reads.fastq.gz" # Placeholder for adapter-trimmed RNAseq reads
   STAR_INDEX_DIR="repbase_star_index" # Placeholder for STAR index of RepBase v18.05 human repetitive elements
   OUTPUT_DIR="star_mapping_repbase"
   
   # Create output directory
   mkdir -p "${OUTPUT_DIR}"
   
   # Run STAR for mapping
   STAR \
     --genomeDir "${STAR_INDEX_DIR}" \
     --readFilesIn "${READS_FILE}" \
     --runThreadN 8 \
     --outFileNamePrefix "${OUTPUT_DIR}/" \
     --outSAMtype BAM SortedByCoordinate

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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 v2.5.2b GitHub

   $ Bash example

   # Install STAR if not already installed
   # conda install -c bioconda star
   
   # --- Prepare STAR genome index (run once) ---
   # Replace /path/to/hg19_fasta and /path/to/hg19_gtf with actual paths
   # mkdir -p /path/to/STAR_index/hg19
   # STAR --runThreadN 16 \
   #      --runMode genomeGenerate \
   #      --genomeDir /path/to/STAR_index/hg19 \
   #      --genomeFastaFiles /path/to/hg19_fasta/hg19.fa \
   #      --sjdbGTFfile /path/to/hg19_gtf/hg19.gtf \
   #      --sjdbOverhang 100 # Recommended: read_length - 1
   #      # For ENCODE-like pipelines, additional parameters might be used for genome generation,
   #      # e.g., --genomeSAindexNbases 14 for smaller genomes or specific applications.
   
   # --- Align reads with STAR ---
   # Input FASTQ file (assuming it's gzipped and pre-processed if necessary)
   INPUT_FASTQ="input_reads.fastq.gz"
   # Output directory for STAR results
   OUTPUT_DIR="star_output"
   # Prefix for output files
   OUTPUT_PREFIX="${OUTPUT_DIR}/star_aligned"
   # Path to the pre-built STAR genome index for hg19
   GENOME_DIR="/path/to/STAR_index/hg19"
   # Number of threads to use
   NUM_THREADS=16 # Adjust based on available resources
   
   mkdir -p "${OUTPUT_DIR}"
   
   STAR --genomeDir "${GENOME_DIR}" \
        --readFilesIn "${INPUT_FASTQ}" \
        --readFilesCommand zcat \
        --outFileNamePrefix "${OUTPUT_PREFIX}" \
        --runThreadN "${NUM_THREADS}" \
        --outFilterMultimapNmax 1 \
        --outSAMtype BAM SortedByCoordinate \
        --outSAMattributes NH HI AS NM MD

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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 v1.14.6 (Inferred from publication date 2014)

   $ Bash example

   # Install featureCounts (part of Rsubread package)
   # conda install -c bioconda r-rsubread
   
   # Download GENCODE hg19 annotation (release 19 is a common choice for hg19)
   # wget -O gencode.v19.annotation.gtf.gz ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_19/gencode.v19.annotation.gtf.gz
   # gunzip gencode.v19.annotation.gtf.gz
   
   # Run featureCounts to calculate read counts for all genes
   # Assumes 'input.bam' is the aligned BAM file and 'gencode.v19.annotation.gtf' is the unzipped annotation file.
   # -a: Annotation file
   # -F GTF: Specify annotation file format as GTF
   # -t exon: Count features of type 'exon'
   # -g gene_id: Group features by 'gene_id' attribute to count reads per gene
   # -o: Output file for read counts
   featureCounts -a gencode.v19.annotation.gtf -F GTF -t exon -g gene_id -o gene_counts.txt input.bam

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Tools Used