GSE67040 Processing Pipeline

Publication

Nature communications (2022) — PMID 35781533

Dataset

Leucegene: AML sequencing

1. 1

   Basecalls performed using CASAVA 1.8

   CASAVA v1.8

   $ Bash example

   # CASAVA 1.8 is an older Illumina proprietary software suite for base calling and demultiplexing.
   # It typically runs on the instrument or a dedicated server.
   # The primary script for converting BCL files to FASTQ in CASAVA 1.8 was often configureBclToFastq.pl,
   # which was part of the bcl2fastq conversion software.
   # This command is a generic example; actual parameters depend on the specific run and setup.
   
   # Example command for base calling and demultiplexing using CASAVA's bcl2fastq component:
   configureBclToFastq.pl --input-dir /path/to/illumina/run/folder --output-dir /path/to/output/fastq/files --mismatches 1

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2. 2

   Sequenced reads were mapped to the reference genome using CASAVA 1.8

   CASAVA v1.8

   $ Bash example

   # CASAVA 1.8 was an integrated Illumina software suite for base calling, demultiplexing, alignment, and variant calling.
   # The mapping of sequenced reads to a reference genome was an internal step within its larger workflow.
   # A direct, standalone command for "mapping" is not typically exposed for CASAVA 1.8.
   
   # Placeholder for reference genome (e.g., GRCh38/hg38).
   # If not specified in the description, use the latest assembly as a placeholder.
   REFERENCE_GENOME="path/to/GRCh38.fasta"
   
   # Placeholder for input FASTQ files directory.
   # CASAVA often processed a directory containing FASTQ files for multiple samples.
   FASTQ_INPUT_DIR="path/to/sequenced_reads_fastq_directory"
   
   # Placeholder for the output directory where CASAVA will place its results, including aligned BAM files.
   CASAVA_OUTPUT_DIR="path/to/casava_output_directory"
   
   # Placeholder for a CASAVA configuration file (e.g., XML or a custom format).
   # This file would specify parameters for the build, including alignment settings.
   CASAVA_BUILD_CONFIG="path/to/casava_build_configuration.xml"
   
   # The 'configureBuild.pl' script was used to set up the alignment and variant calling pipeline.
   # This command initiates the CASAVA 1.8 build process that includes mapping.
   # It generates a Makefile and other necessary files in the output directory.
   configureBuild.pl \
       --fastq-dir ${FASTQ_INPUT_DIR} \
       --output-dir ${CASAVA_OUTPUT_DIR} \
       --genome ${REFERENCE_GENOME} \
       --config ${CASAVA_BUILD_CONFIG}
   
   # After configuration, one would typically navigate to the generated output directory
   # and run 'make' to execute the pipeline, which includes the alignment step.
   # cd ${CASAVA_OUTPUT_DIR}
   # make

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3. 3

   Reads Per Kilobase of exon per Megabase of library size (RPKM) were calculated using CASAVA 1.8

   RSEM (Inferred with models/gemini-2.5-flash) v1.3.3 GitHub

   $ Bash example

   # Install RSEM (if not already installed)
   # conda install -c bioconda rsem
   # conda install -c bioconda star # RSEM often uses STAR internally for alignment if given FASTQ
   
   # Define reference genome and annotation (using GRCh38 and GENCODE v38 as placeholders)
   # Download GRCh38 primary assembly FASTA
   # wget -O GRCh38.primary_assembly.genome.fa.gz "http://ftp.ensembl.org/pub/release-109/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz"
   # gunzip GRCh38.primary_assembly.genome.fa.gz
   # Download GENCODE v38 annotation GTF
   # wget -O gencode.v38.annotation.gtf.gz "http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_38/gencode.v38.annotation.gtf.gz"
   # gunzip gencode.v38.annotation.gtf.gz
   
   GENOME_FASTA="GRCh38.primary_assembly.genome.fa"
   GTF_FILE="gencode.v38.annotation.gtf"
   RSEM_REF_DIR="rsem_ref_GRCh38_gencode38"
   
   # Build RSEM reference (this step needs to be done once)
   # rsem-prepare-reference --gtf "${GTF_FILE}" "${GENOME_FASTA}" "${RSEM_REF_DIR}"
   
   # Input BAM file (assuming it's already aligned, e.g., by STAR, and processed from CASAVA 1.8 FASTQs)
   # Replace 'sample.aligned.bam' with the actual path to your aligned BAM file.
   INPUT_BAM="sample.aligned.bam"
   
   # Output directory and prefix for RSEM results
   OUTPUT_DIR="rsem_quantification"
   SAMPLE_PREFIX="sample_rpkm"
   
   mkdir -p "${OUTPUT_DIR}"
   
   # Run RSEM quantification to calculate RPKM
   # --bam: Specifies that the input is a BAM file
   # --no-qualities: Assumes BAM file might not have quality scores (common for some aligners or older data)
   # --strandedness none: Assumes unstranded library. Adjust to 'forward' or 'reverse' if known.
   # -p 8: Use 8 threads for parallel processing
   # The output files will be in ${OUTPUT_DIR} with prefix ${SAMPLE_PREFIX}
   # RPKM values are found in the .genes.results file (column 6) and .isoforms.results file (column 6)
   
   rsem-calculate-expression \
       --bam \
       --no-qualities \
       --strandedness none \
       -p 8 \
       "${INPUT_BAM}" \
       "${RSEM_REF_DIR}" \
       "${OUTPUT_DIR}/${SAMPLE_PREFIX}"
   
   echo "RPKM values are available in ${OUTPUT_DIR}/${SAMPLE_PREFIX}.genes.results (column 6) and ${OUTPUT_DIR}/${SAMPLE_PREFIX}.isoforms.results (column 6)."

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