GSE201897 Processing Pipeline

Publication

Science translational medicine (2022) — PMID 35767654

Dataset

MECP2-related pathways are dysregulated in a cortical organoid model of Myotonic dystrophy [bulk RNA-Seq]

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 available
   # conda install -c bioconda star=2.4.0i
   
   # --- Reference Data Preparation (Conceptual) ---
   # The description states mapping to "human-specific repetitive elements from RepBase (version 18.05)".
   # This implies a STAR genome index was generated using these sequences.
   # Example command to generate such an index (assuming 'repbase_18.05_human_repeats.fasta' is the FASTA file):
   # STAR --runMode genomeGenerate \
   #      --genomeDir /path/to/STAR_RepBase_18.05_Index \
   #      --genomeFastaFiles /path/to/repbase_18.05_human_repeats.fasta \
   #      --runThreadN 8 # Adjust thread count as needed
   
   # --- Alignment Step ---
   # Assuming adapter-trimmed RNAseq reads are available (e.g., from Cutadapt)
   # and a STAR genome index for RepBase 18.05 human repetitive elements has been generated.
   
   # Define variables for clarity
   STAR_GENOME_DIR="/path/to/STAR_RepBase_18.05_Index" # Placeholder for the RepBase genome index
   TRIMMED_READS_R1="sample_trimmed_R1.fastq.gz" # Placeholder for trimmed forward reads
   TRIMMED_READS_R2="sample_trimmed_R2.fastq.gz" # Placeholder for trimmed reverse reads (adjust for single-end if needed)
   OUTPUT_PREFIX="sample_repbase_alignment"
   NUM_THREADS=8 # Adjust thread count as needed
   
   STAR --genomeDir "${STAR_GENOME_DIR}" \
        --readFilesIn "${TRIMMED_READS_R1}" "${TRIMMED_READS_R2}" \
        --runThreadN "${NUM_THREADS}" \
        --outFileNamePrefix "${OUTPUT_PREFIX}_" \
        --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 v2.7.10a GitHub

   $ Bash example

   # Install STAR (if not already installed)
   # conda install -c bioconda star
   
   # Define variables
   # Replace with the actual path to your pre-built hg19 STAR genome index
   GENOME_DIR="/path/to/STAR_index/hg19"
   # Replace with your input R1 FASTQ file (e.g., after repeat-mapping reads removal)
   READS_R1="sample_R1.fastq.gz"
   # Replace with your input R2 FASTQ file (remove this line if single-end reads)
   READS_R2="sample_R2.fastq.gz"
   OUTPUT_PREFIX="sample_aligned_"
   THREADS=16 # Adjust based on available CPU cores
   
   # Note: The description states "Repeat-mapping reads were removed".
   # This command uses --outFilterMultimapNmax 1 to ensure only uniquely mapping reads are reported by STAR,
   # aligning with the pre-processing step of removing repeat-mapping reads.
   
   # Run STAR alignment
   STAR \
     --genomeDir "${GENOME_DIR}" \
     --readFilesIn "${READS_R1}" "${READS_R2}" \
     --readFilesCommand zcat \
     --runThreadN "${THREADS}" \
     --outFileNamePrefix "${OUTPUT_PREFIX}" \
     --outSAMtype BAM SortedByCoordinate \
     --outSAMattributes Standard \
     --outFilterMultimapNmax 1 \
     --outFilterType BySJout \
     --outFilterScoreMinOverLread 0.3 \
     --outFilterMatchNminOverLread 0.3 \
     --alignSJDBoverhangMin 1 \
     --alignSJoverhangMin 8 \
     --alignIntronMin 20 \
     --alignIntronMax 1000000 \
     --alignMatesGapMax 1000000 \
     --limitBAMsortRAM 30000000000 # Adjust based on available RAM (e.g., 30GB)

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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 v1.4.6-p5

   $ Bash example

   # Install Subread (which includes featureCounts)
   # conda install -c bioconda subread
   
   # Download GENCODE hg19 annotation (if not already available)
   # For example, from GENCODE archive:
   # wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_19/gencode.v19.annotation.gtf.gz
   # gunzip gencode.v19.annotation.gtf.gz
   
   GENCODE_GTF="gencode.v19.annotation.gtf" # Path to your GENCODE hg19 GTF file
   INPUT_BAM="input.bam" # Placeholder for your aligned BAM file(s)
   OUTPUT_COUNTS="gene_counts.txt"
   
   # Calculate read counts for all genes using featureCounts
   # -a: Annotation file
   # -o: Output file
   # -F GTF: Specify GTF format for the annotation file
   # -t exon: Specify 'exon' as the feature type to count
   # -g gene_id: Specify 'gene_id' as the attribute to group features by (summarizes exon counts to gene level)
   # -s 0: Assume unstranded data (change to 1 for stranded, 2 for reverse stranded if applicable)
   # -T 8: Use 8 threads (adjust as needed for performance)
   featureCounts -a ${GENCODE_GTF} -o ${OUTPUT_COUNTS} -F GTF -t exon -g gene_id -s 0 -T 8 ${INPUT_BAM}

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Tools Used