GSE51684 Processing Pipeline

1. 1

   Illumina Casava1.8.2 software used for basecalling.

   Illumina Casava v1.8.2

   $ Bash example

   # Illumina Casava 1.8.2 was the proprietary software suite used on Illumina sequencers
   # for basecalling and initial demultiplexing. This step is performed by the sequencing
   # instrument itself, generating BCL files which are then converted to FASTQ files
   # using tools like bcl2fastq. There is no direct command-line execution for Casava 1.8.2
   # by the user post-sequencing.

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2. 2

   Demultiplexing based on index sequences

   fastp (Inferred with models/gemini-2.5-flash) v0.23.2 GitHub

   $ Bash example

   # Install fastp if not already available
   # conda install -c bioconda fastp
   
   # Define input and output paths
   # MULTIPLEXED_FASTQ: The input FASTQ file containing reads from multiple samples, each with an in-line barcode.
   # BARCODE_FILE: A tab-separated file where each line contains 'barcode_sequence\tsample_name'.
   #               Example: 
   #               GATACA\tsample_A
   #               CGTTAG\tsample_B
   # OUTPUT_DIR: Directory where demultiplexed FASTQ files will be saved.
   # REPORT_PREFIX: Prefix for the JSON and HTML reports generated by fastp.
   MULTIPLEXED_FASTQ="input_multiplexed_reads.fastq.gz"
   BARCODE_FILE="sample_barcodes.tsv"
   OUTPUT_DIR="demultiplexed_fastqs"
   REPORT_PREFIX="demultiplexing_report"
   
   mkdir -p "${OUTPUT_DIR}"
   
   # Execute fastp for demultiplexing based on in-line index sequences (barcodes).
   # -i: Input FASTQ file (can be gzipped).
   # -o: Output FASTQ file pattern. fastp will replace '{barcode}' with the sample name from BARCODE_FILE.
   # --barcode_file: Specifies the file containing barcode sequences and corresponding sample names.
   # --json, --html: Generate detailed reports in JSON and HTML formats.
   # --thread: Number of threads to use for processing.
   fastp \
     -i "${MULTIPLEXED_FASTQ}" \
     -o "${OUTPUT_DIR}/{barcode}.fastq.gz" \
     --barcode_file "${BARCODE_FILE}" \
     --json "${REPORT_PREFIX}.json" \
     --html "${REPORT_PREFIX}.html" \
     --thread 8

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3. 3

   Sequenced reads were mapped to Refseq RNA sequences using bowtie v0.12.7 with parameters -q -e 100 -m 10 --best --strata

   Bowtie v0.12.7 GitHub

   $ Bash example

   # Install Bowtie (example using conda)
   # conda install -c bioconda bowtie=0.12.7
   
   # Assuming 'refseq_rna_index' is the basename for the Bowtie index files
   # and 'reads.fastq' is the input sequenced reads file.
   # The output will be a SAM file redirected to 'output.sam'.
   bowtie -q -e 100 -m 10 --best --strata refseq_rna_index reads.fastq > output.sam

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4. 4

   Count reads for each genes

   featureCounts (Inferred with models/gemini-2.5-flash) v2.0.1 GitHub

   $ Bash example

   # Install Subread (which includes featureCounts) if not already installed
   # conda install -c bioconda subread
   
   # Define input and output files
   INPUT_BAM="aligned_reads.bam" # Replace with your actual aligned BAM file
   GENE_ANNOTATION_GTF="Homo_sapiens.GRCh38.109.gtf" # Replace with your actual GTF file (e.g., from Ensembl or GENCODE)
   OUTPUT_COUNTS_FILE="gene_counts.txt"
   NUM_THREADS=8 # Adjust as needed
   
   # Count reads for each gene using featureCounts
   # -a: Annotation file (GTF/GFF format)
   # -o: Output file for read counts
   # -F GTF: Specify that the annotation file is in GTF format
   # -t exon: Specify feature type to count (e.g., "exon")
   # -g gene_id: Specify attribute to group features by (e.g., "gene_id")
   # -s 2: Reverse stranded library (common for eCLIP assays)
   # -T: Number of threads
   # --primary: Only count primary alignments (useful for multi-mapping reads)
   featureCounts -a "${GENE_ANNOTATION_GTF}" \
                 -o "${OUTPUT_COUNTS_FILE}" \
                 -F GTF \
                 -t exon \
                 -g gene_id \
                 -s 2 \
                 -T "${NUM_THREADS}" \
                 --primary \
                 "${INPUT_BAM}"

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Tools Used