GSE224548 Processing Pipeline

Publication

Blood cancer discovery (2023) — PMID 36763002

Dataset

A two-step in vivo CRISPR screen unveils pervasive RNA binding protein dependencies for leukemic stem cells and identifies ELAVL1 as a therapeutic ta…

1. 1

   Reads were quality checked using fastQC

   FastQC v0.11.9 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install FastQC using Conda
   # conda create -n fastqc_env fastqc -c bioconda -y
   # conda activate fastqc_env
   
   # Run FastQC on input reads
   # Replace reads.fastq.gz with your actual input file(s)
   # Replace output_dir with your desired output directory
   mkdir -p output_dir
   fastqc reads.fastq.gz -o output_dir

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2. 2

   Sequencing reads were aligned to the hg38 reference genome using STAR (v2.7.2c)

   STAR v2.7.2c GitHub

   $ Bash example

   # Install STAR (if not already installed)
   # conda install -c bioconda star
   
   # --- Reference Data Setup ---
   # Download hg38 genome FASTA and GTF files (example from UCSC)
   # wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz
   # wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/genes/hg38.ncbiRefSeq.gtf.gz
   # gunzip hg38.fa.gz
   # gunzip hg38.ncbiRefSeq.gtf.gz
   
   # Create STAR genome index (if not already available)
   # mkdir -p /path/to/star_index/hg38
   # STAR --runMode genomeGenerate \
   #      --genomeDir /path/to/star_index/hg38 \
   #      --genomeFastaFiles hg38.fa \
   #      --sjdbGTFfile hg38.ncbiRefSeq.gtf \
   #      --sjdbOverhang 100 \
   #      --runThreadN 16
   
   # --- Alignment Step ---
   # Define input files and output prefix
   INPUT_R1="input_R1.fastq.gz"
   INPUT_R2="input_R2.fastq.gz" # Remove if single-end
   GENOME_DIR="/path/to/star_index/hg38"
   OUTPUT_PREFIX="aligned_reads_"
   THREADS=8
   
   # Execute STAR alignment
   STAR --genomeDir "${GENOME_DIR}" \
        --readFilesIn "${INPUT_R1}" "${INPUT_R2}" \
        --runThreadN "${THREADS}" \
        --outFileNamePrefix "${OUTPUT_PREFIX}" \
        --outSAMtype BAM SortedByCoordinate \
        --outSAMunmapped Within \
        --outSAMattributes Standard \
        --quantMode GeneCounts \
        --twopassMode Basic

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3. 3

   Gene-level quantification was performed using RSEM (v1.3.1)

   RSEM v1.3.1 GitHub

   $ Bash example

   # Install RSEM (example using conda)
   # conda install -c bioconda rsem
   
   # Placeholder for RSEM reference index.
   # This index should have been built previously using 'rsem-prepare-reference'.
   # Replace '/path/to/rsem_reference/human_GRCh38' with the actual path to your RSEM reference.
   RSEM_REFERENCE="/path/to/rsem_reference/human_GRCh38"
   
   # Input aligned reads file (e.g., BAM).
   # Replace 'input.bam' with your actual input file.
   INPUT_BAM="input.bam"
   
   # Output prefix for RSEM results (e.g., gene_quantification_results.genes.results, .isoforms.results)
   OUTPUT_PREFIX="gene_quantification_results"
   
   # Perform gene-level quantification using rsem-calculate-expression.
   # This command assumes:
   # 1. Input is a BAM file (--bam).
   # 2. Reads are paired-end (--paired-end).
   # Adjust parameters if your input is FASTQ, single-end, or requires specific options (e.g., --strandedness).
   rsem-calculate-expression \
       --bam \
       --paired-end \
       "${INPUT_BAM}" \
       "${RSEM_REFERENCE}" \
       "${OUTPUT_PREFIX}"

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Tools Used