GSE207251 Processing Pipeline

Publication

Neurobiology of disease (2022) — PMID 35905927

Dataset

Prions induce an early Arc response in the hippocampus of prion-infected mice

1. 1

   Adapters trimmed with cutadapt (v1.4.0)

   cutadapt v1.4.0 GitHub

   $ Bash example

   # Install cutadapt (if not already installed)
   # conda install -c bioconda cutadapt=1.4.0
   
   # Define adapter sequences based on common Illumina TruSeq adapters used in eCLIP assays.
   # These sequences are often found in the Yeo lab's eCLIP CWL workflow (e.g., https://github.com/yeolab/eclip/blob/master/eclip.cwl).
   ADAPTER_3_PRIME="AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC" # Illumina TruSeq 3' adapter
   ADAPTER_5_PRIME="AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT" # Illumina TruSeq 5' adapter
   
   # Define placeholder input and output files
   INPUT_FASTQ="input_reads.fastq.gz"
   OUTPUT_FASTQ="trimmed_reads.fastq.gz"
   
   # Parameters inferred from common eCLIP preprocessing steps:
   # -a: 3' adapter sequence to be removed from the 3' end of reads.
   # -g: 5' adapter sequence to be removed from the 5' end of reads.
   # -u 6: Trim 6 bases from the 5' end of reads (common for UMI or random primer removal in eCLIP).
   # -q 20: Quality trim from the 3' end using a quality threshold of 20.
   # -m 18: Discard reads shorter than 18 bp after trimming.
   # -o: Specify the output file.
   # Note: --cores or parallel processing options were not available in cutadapt v1.4.0.
   
   cutadapt -a "${ADAPTER_3_PRIME}" \
            -g "${ADAPTER_5_PRIME}" \
            -u 6 \
            -q 20 \
            -m 18 \
            -o "${OUTPUT_FASTQ}" \
            "${INPUT_FASTQ}"

   Copy View on GitHub
2. 2

   Reads mapping to repetitive elements (RepBase v18.04) with STAR were removed

   STAR v2.7.3a GitHub

   $ Bash example

   # --- Installation (commented out) ---
   # conda create -n star_env star -y
   # conda activate star_env
   
   # --- Reference Data Setup (commented out, assuming RepBase index is pre-built) ---
   # Download RepBase v18.04 FASTA (e.g., from GIRINST or a local mirror)
   # Example: wget -O repbase_v18.04.fasta "https://www.girinst.org/repbase/update/RepBase_v18.04.fasta.gz"
   # gunzip repbase_v18.04.fasta.gz
   
   # Build STAR index for RepBase v18.04
   # REPBASE_FASTA="repbase_v18.04.fasta" # Path to RepBase v18.04 FASTA file
   # REPBASE_STAR_INDEX="/path/to/repbase_star_index" # Directory for the STAR index
   # mkdir -p "${REPBASE_STAR_INDEX}"
   # STAR --runMode genomeGenerate \
   #      --genomeDir "${REPBASE_STAR_INDEX}" \
   #      --genomeFastaFiles "${REPBASE_FASTA}" \
   #      --genomeSAindexNbases 10 # Recommended for smaller genomes/decoy sequences
   
   # --- Parameters ---
   READ1="input_R1.fastq.gz" # Path to input Read 1 FASTQ file
   READ2="input_R2.fastq.gz" # Path to input Read 2 FASTQ file (remove if single-end)
   REPBASE_STAR_INDEX="/path/to/repbase_star_index" # Path to the pre-built STAR index for RepBase v18.04
   OUTPUT_PREFIX="filtered_from_repbase" # Prefix for output files
   THREADS=8 # Number of threads to use
   
   # --- Execution Command ---
   # Align reads to the RepBase index. Reads that map to repetitive elements are effectively "removed"
   # by outputting only the unmapped reads for subsequent analysis.
   STAR --genomeDir "${REPBASE_STAR_INDEX}" \
        --readFilesIn "${READ1}" "${READ2}" \
        --readFilesCommand zcat \
        --outFileNamePrefix "${OUTPUT_PREFIX}_" \
        --outSAMtype None \
        --outReadsUnmapped Fastx \
        --outFilterMultimapNmax 100 \
        --outFilterMismatchNmax 999 \
        --outFilterMismatchNoverLmax 1 \
        --outFilterScoreMinOverLread 0 \
        --outFilterMatchNminOverLread 0 \
        --alignIntronMin 20 \
        --alignIntronMax 1000000 \
        --alignMatesGapMax 1000000 \
        --alignSJoverhangMin 8 \
        --alignSJDBoverhangMin 1 \
        --sjdbScore 1 \
        --runThreadN "${THREADS}"
   
   # Output files:
   # - ${OUTPUT_PREFIX}_Unmapped.out.mate1: FASTQ file containing Read 1 sequences that did NOT map to RepBase.
   # - ${OUTPUT_PREFIX}_Unmapped.out.mate2: FASTQ file containing Read 2 sequences that did NOT map to RepBase (if paired-end).
   # These unmapped reads are the ones that proceed to the next step of the pipeline (e.g., alignment to the main genome).

   Copy View on GitHub
3. 3

   Remaining reads were mapped to mouse genome mm9 with STAR (v2.4.0i)

   STAR v2.4.0i GitHub

   $ Bash example

   # Install STAR if not already installed
   # conda install -c bioconda star=2.4.0i
   
   # Define variables (adjust paths as needed)
   # Placeholder for input reads. "Remaining reads" implies a single FASTQ or gzipped FASTQ.
   INPUT_FASTQ="remaining_reads.fastq.gz"
   # Placeholder for the STAR genome index directory for mouse genome mm9.
   # This directory should contain genome.fa, SA, SAindex, etc., pre-built by STAR --runMode genomeGenerate.
   # Example: /path/to/star_index/mm9
   GENOME_DIR="/path/to/star_index/mm9"
   # Prefix for output files (e.g., mapped_reads.Aligned.sortedByCoord.out.bam)
   OUTPUT_PREFIX="mapped_reads"
   # Number of threads to use for alignment
   NUM_THREADS=8 # Adjust based on available CPU cores
   
   # Run STAR alignment
   STAR --genomeDir "${GENOME_DIR}" \
        --readFilesIn "${INPUT_FASTQ}" \
        --runThreadN "${NUM_THREADS}" \
        --outFileNamePrefix "${OUTPUT_PREFIX}" \
        --outSAMtype BAM SortedByCoordinate # Common output format for downstream analysis

   Copy View on GitHub
4. 4

   FeatureCounts was used to assign reads to genes (v1.5.0)

   featureCounts v1.5.0

   $ Bash example

   # Install featureCounts (part of the Subread package)
   # conda install -c bioconda subread
   
   # Example: Assign reads from an RNA-seq BAM file to genes using a GTF annotation.
   # This command assumes unstranded data (-s 0). Adjust -s 1 or -s 2 for stranded libraries.
   # Replace 'Homo_sapiens.GRCh38.109.gtf' with your actual gene annotation file (e.g., from Ensembl or GENCODE).
   # Replace 'aligned_reads.bam' with your input BAM file(s).
   # Replace 'gene_counts.txt' with your desired output file name.
   featureCounts -a Homo_sapiens.GRCh38.109.gtf \
                 -o gene_counts.txt \
                 -F GTF \
                 -t exon \
                 -g gene_id \
                 -s 0 \
                 -T 8 \
                 aligned_reads.bam

   Copy

Tools Used