GSE153264 Processing Pipeline

Publication

Cell reports (2022) — PMID 35263585

Dataset

Definition of a Small Core Transcriptional Circuit Regulated by AML1-ETO [RNA-seq]

1. 1

   Adaptors were trimmed using trimmomatic (SE for Kasumi-1 cells, PE for CD34+ cells).

   Trimmomatic v0.39

   $ Bash example

   # Install Trimmomatic (if not already installed)
   # conda install -c bioconda trimmomatic=0.39
   
   # Define Trimmomatic path and adapter file paths
   TRIMMOMATIC_JAR="/path/to/trimmomatic-0.39.jar" # Adjust path to your Trimmomatic .jar file
   ADAPTER_FILE_PE="/path/to/Trimmomatic/adapters/TruSeq3-PE.fa" # Path to Illumina adapter file for Paired-End
   ADAPTER_FILE_SE="/path/to/Trimmomatic/adapters/TruSeq3-SE.fa" # Path to Illumina adapter file for Single-End
   THREADS=8 # Number of threads to use
   
   # --- Scenario 1: Single-End (SE) for Kasumi-1 cells ---
   # Assuming input file is kasumi1_raw.fastq.gz
   INPUT_SE_FASTQ="kasumi1_raw.fastq.gz"
   OUTPUT_SE_TRIMMED="kasumi1_trimmed.fastq.gz"
   
   echo "Trimming adapters for Single-End reads (Kasumi-1 cells)..."
   java -jar "${TRIMMOMATIC_JAR}" SE -phred33 \
       "${INPUT_SE_FASTQ}" \
       "${OUTPUT_SE_TRIMMED}" \
       ILLUMINACLIP:"${ADAPTER_FILE_SE}":2:30:10 \
       LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36 \
       -threads "${THREADS}"
   
   echo ""
   
   # --- Scenario 2: Paired-End (PE) for CD34+ cells ---
   # Assuming input files are cd34_R1_raw.fastq.gz and cd34_R2_raw.fastq.gz
   INPUT_PE_R1="cd34_R1_raw.fastq.gz"
   INPUT_PE_R2="cd34_R2_raw.fastq.gz"
   OUTPUT_PE_R1_PAIRED="cd34_R1_paired.fastq.gz"
   OUTPUT_PE_R1_UNPAIRED="cd34_R1_unpaired.fastq.gz"
   OUTPUT_PE_R2_PAIRED="cd34_R2_paired.fastq.gz"
   OUTPUT_PE_R2_UNPAIRED="cd34_R2_unpaired.fastq.gz"
   
   echo "Trimming adapters for Paired-End reads (CD34+ cells)..."
   java -jar "${TRIMMOMATIC_JAR}" PE -phred33 \
       "${INPUT_PE_R1}" "${INPUT_PE_R2}" \
       "${OUTPUT_PE_R1_PAIRED}" "${OUTPUT_PE_R1_UNPAIRED}" \
       "${OUTPUT_PE_R2_PAIRED}" "${OUTPUT_PE_R2_UNPAIRED}" \
       ILLUMINACLIP:"${ADAPTER_FILE_PE}":2:30:10 \
       LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36 \
       -threads "${THREADS}"

   Copy
2. 2

   Trimmed reads were aligned to the human genome (hg19) with TopHat v2.0.11.

   TopHat v2.0.11 GitHub

   $ Bash example

   # Install TopHat v2.0.11
   # conda create -n tophat_env tophat=2.0.11 -y
   # conda activate tophat_env
   
   # --- Reference Genome Preparation (if not already done) ---
   # TopHat v2 uses Bowtie2 for alignment. Ensure hg19 Bowtie2 index is available.
   # Download hg19 reference genome FASTA
   # mkdir -p ref/hg19
   # wget -P ref/hg19 http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz
   # gunzip ref/hg19/hg19.fa.gz
   
   # Build Bowtie2 index for hg19
   # bowtie2-build ref/hg19/hg19.fa ref/hg19/hg19_index
   
   # --- Alignment Step ---
   # Define input and output paths
   INPUT_READS="trimmed_reads.fastq" # Placeholder for your trimmed reads file
   GENOME_INDEX_PREFIX="ref/hg19/hg19_index" # Path to the Bowtie2 index prefix for hg19
   OUTPUT_DIR="tophat_alignment_hg19"
   
   # Run TopHat alignment
   # -o: Output directory
   # -p: Number of threads (adjust as needed)
   tophat -o "${OUTPUT_DIR}" -p 8 "${GENOME_INDEX_PREFIX}" "${INPUT_READS}"

   Copy View on GitHub
3. 3

   Differential gene expression was determined using Cuffdiff v.2.1.1.

   Cufflinks v2.1.1 GitHub

   $ Bash example

   # Install Cufflinks (which includes Cuffdiff)
   # conda install -c bioconda cufflinks=2.1.1
   
   # Define variables for input and output
   # TRANSCRIPTS_GTF is typically generated by Cufflinks or StringTie from aligned reads
   TRANSCRIPTS_GTF="path/to/merged_transcripts.gtf"
   
   # Comma-separated BAM files for each condition/sample group
   # Replace with actual paths to your aligned BAM files
   SAMPLE1_BAMS="path/to/sample1_rep1.bam,path/to/sample1_rep2.bam"
   SAMPLE2_BAMS="path/to/sample2_rep1.bam,path/to/sample2_rep2.bam"
   
   OUTPUT_DIR="cuffdiff_output"
   
   # Placeholder for reference genome FASTA and annotation GTF
   # Use the latest assembly (e.g., GRCh38/hg38) and corresponding annotation (e.g., Ensembl, Gencode)
   GENOME_FASTA="path/to/GRCh38.fa"
   GENOME_ANNOTATION_GTF="path/to/GRCh38.gtf"
   
   # Create output directory
   mkdir -p "${OUTPUT_DIR}"
   
   # Run Cuffdiff for differential expression analysis
   # -o: output directory
   # -L: comma-separated list of condition labels (must match the order of BAM groups)
   # -b: enable bias correction using a reference genome FASTA file
   # -u: use a reference annotation GTF to guide assembly and quantification
   cuffdiff -o "${OUTPUT_DIR}" \
            -L "ConditionA,ConditionB" \
            -b "${GENOME_FASTA}" \
            -u "${GENOME_ANNOTATION_GTF}" \
            "${TRANSCRIPTS_GTF}" \
            "${SAMPLE1_BAMS}" \
            "${SAMPLE2_BAMS}"
   

   Copy View on GitHub

Tools Used