GSE54968 Processing Pipeline

Publication

Circulation (2015) — PMID 25739401

Dataset

Transcriptomic analysis reveals novel long non-coding RNAs critical for vertebrate development [RNA-Seq]

1. 1

   Illumina Casava1.7 software used for basecalling.

   Illumina Casava v1.7 GitHub

   $ Bash example

   # Illumina Casava 1.7 is proprietary software typically integrated with Illumina sequencing instruments.
   # Basecalling is performed by the instrument's control software, which utilizes Casava components.
   # The output of this step is BCL (base call) files.
   # No direct command-line execution is typically performed by users for the basecalling itself,
   # as it's part of the real-time data acquisition on the sequencer.
   # Subsequent steps like demultiplexing and BCL-to-FASTQ conversion would use other Casava tools
   # (e.g., configureBclToFastq.pl and make, or later bcl2fastq).

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2. 2

   Reads were aligned to the human genome (hg19) with STAR (v2.2.0c) with default parameters.

   STAR v2.2.0c GitHub

   $ Bash example

   # Install STAR if not already installed
   # conda install -c bioconda star=2.2.0c
   
   # Define variables
   # Replace /path/to/your/hg19/STAR_index with the actual path to your STAR genome index for hg19.
   # This index needs to be built once using STAR --runMode genomeGenerate.
   GENOME_DIR="/path/to/your/hg19/STAR_index"
   READS_FILE="reads.fastq.gz" # Replace with your input FASTQ file (e.g., R1.fastq.gz or R2.fastq.gz for single-end, or R1.fastq.gz R2.fastq.gz for paired-end)
   OUTPUT_PREFIX="star_output/" # Output files will be prefixed with this path
   THREADS=8 # Number of threads to use, adjust based on available CPU cores
   
   # Create output directory if it doesn't exist
   mkdir -p "${OUTPUT_PREFIX}"
   
   # Run STAR alignment with default parameters
   # The --outSAMtype BAM SortedByCoordinate is a common and practical default for downstream analysis.
   STAR \
     --genomeDir "${GENOME_DIR}" \
     --readFilesIn "${READS_FILE}" \
     --runThreadN "${THREADS}" \
     --outFileNamePrefix "${OUTPUT_PREFIX}" \
     --outSAMtype BAM SortedByCoordinate \
     --outSAMattributes Standard
   

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3. 3

   Only uniquely alignable reads were used for downstream analysis

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

   $ Bash example

   # Install STAR if not already installed
   # conda install -c bioconda star
   
   # Create a directory for STAR output
   mkdir -p star_output
   
   # Align reads and filter for uniquely mapping reads
   # Replace /path/to/STAR_genome_index with the actual path to your STAR genome index
   # Replace reads_R1.fastq.gz and reads_R2.fastq.gz with your input FASTQ files
   # Adjust --runThreadN based on available CPU cores
   # Adjust --limitBAMsortRAM based on available RAM (e.g., 30GB for 30000000000 bytes)
   STAR \
     --runThreadN 8 \
     --genomeDir /path/to/STAR_genome_index \
     --readFilesIn reads_R1.fastq.gz reads_R2.fastq.gz \
     --readFilesCommand zcat \
     --outFileNamePrefix star_output/aligned_unique_reads_ \
     --outSAMtype BAM SortedByCoordinate \
     --outFilterMultimapNmax 1 \
     --outFilterType BySJout \
     --outFilterMismatchNmax 10 \
     --outFilterMismatchNoverLmax 0.04 \
     --alignIntronMin 20 \
     --alignIntronMax 1000000 \
     --alignMatesGapMax 1000000 \
     --limitBAMsortRAM 30000000000

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4. 4

   Reads Per Kilobase of exon per milion mapped reads were calculated using HOMER (http://homer.salk.edu/homer/) across the entire gene (both introns and exons)

   HOMER vv4.11

   $ Bash example

   # Install HOMER (if not already installed)
   # Download from http://homer.salk.edu/homer/software/install.html
   # Or, if available via a package manager (less common for HOMER's full suite):
   # conda install -c bioconda homer
   
   # Define input and output paths
   INPUT_BAM="path/to/your/aligned_reads.bam"
   OUTPUT_TAG_DIR="homer_tag_directory"
   GENOME_BUILD="hg38" # Example: hg38 for human, mm10 for mouse. Ensure HOMER has this genome installed.
   OUTPUT_RPKM_FILE="gene_rpkm_quantification.txt"
   
   # Step 1: Create a HOMER Tag Directory from the BAM file
   # This step processes the aligned reads into a format HOMER can use efficiently.
   # -format sam: Specifies input format (BAM is compatible with SAM format)
   makeTagDirectory "${OUTPUT_TAG_DIR}" "${INPUT_BAM}" -format sam
   
   # Step 2: Calculate RPKM across genes using annotatePeaks.pl
   # "genes": Tells HOMER to use its internal gene database for annotation.
   # "${GENOME_BUILD}": Specifies the genome build for the gene database.
   # "${OUTPUT_TAG_DIR}": The tag directory created in Step 1.
   # -gene: Annotate with gene information.
   # -rpkm: Calculate RPKM (Reads Per Kilobase of exon per Million mapped reads).
   #        HOMER's -rpkm option calculates the length of the gene from the total length of exons.
   #        Reads mapping within the gene body (introns and exons) are counted for the numerator.
   annotatePeaks.pl "genes" "${GENOME_BUILD}" "${OUTPUT_TAG_DIR}" -gene -rpkm > "${OUTPUT_RPKM_FILE}"

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Tools Used