GSE113947 Processing Pipeline

Publication

Nature communications (2021) — PMID 34732726

Dataset

RNA-Seq of isogenic human iPS cell-derived cardiomyocytes with RBM20 mutations created by genome editing [RNA-Seq]

1. 1

   RNA-Seq data was aligned to the human hg19 reference genome and transcriptome (Ensembl 72) using the software STAR.

   STAR v2.4.2a GitHub

   $ Bash example

   # Install STAR (if not already installed)
   # conda install -c bioconda star
   
   # Define variables for input and output
   # Replace with actual paths and filenames
   # The GENOME_DIR should contain a STAR index built from the human hg19 genome and Ensembl 72 transcriptome annotation.
   GENOME_DIR="/path/to/STAR_index/human_hg19_ensembl72"
   READ1_FASTQ="input_R1.fastq.gz"
   READ2_FASTQ="input_R2.fastq.gz" # Remove this line if data is single-end
   OUTPUT_PREFIX="sample_aligned_"
   NUM_THREADS=8 # Adjust based on available CPU cores
   
   # Align RNA-Seq data to the human hg19 reference genome and transcriptome (Ensembl 72)
   STAR \
     --genomeDir "${GENOME_DIR}" \
     --readFilesIn "${READ1_FASTQ}" "${READ2_FASTQ}" \
     --readFilesCommand zcat \
     --outFileNamePrefix "${OUTPUT_PREFIX}" \
     --outSAMtype BAM SortedByCoordinate \
     --outSAMattributes Standard \
     --runThreadN "${NUM_THREADS}"

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2. 2

   To accurately detect diverse alternative splicing events in iPSC-CM RNA-Seq, we adapted a recently developed Percent Spliced In (PSI) splicing method called MultiPath-PSI.

   RNA-seq vv1.0.0

   $ Bash example

   # Install MultiPath-PSI (recommended in a dedicated conda environment)
   # conda create -n multipath_psi python=3.8
   # conda activate multipath_psi
   # conda install -c bioconda multipath-psi
   
   # --- Placeholder for upstream data and configuration ---
   # MultiPath-PSI integrates results from other splicing quantification tools (e.g., rMATS, LeafCutter).
   # The RNA-Seq data (iPSC-CM RNA-Seq) would first be processed by such tools.
   # For example, if rMATS was used, its output files would be the input for MultiPath-PSI.
   
   # Placeholder for reference genome and annotation (used by upstream tools like rMATS or LeafCutter)
   # GENOME_FASTA="/path/to/human/hg38.fa"
   # GENOME_GTF="/path/to/human/gencode.v38.annotation.gtf"
   
   # Placeholder for input files from an upstream splicing quantification tool (e.g., rMATS)
   # Replace with actual paths to your rMATS (or other tool) output files for each sample.
   # For demonstration, we assume rMATS output for 'Skipped Exon' (SE) and 'Mutually Exclusive Exons' (MXE) events.
   # RMATS_OUTPUT_DIR="/path/to/your/rmats_output_directory"
   # SAMPLE1_RMATS_SE_JC="${RMATS_OUTPUT_DIR}/sample1_SE.MATS.JC.txt"
   # SAMPLE1_RMATS_MXE_JC="${RMATS_OUTPUT_DIR}/sample1_MXE.MATS.JC.txt"
   # SAMPLE2_RMATS_SE_JC="${RMATS_OUTPUT_DIR}/sample2_SE.MATS.JC.txt"
   # SAMPLE2_RMATS_MXE_JC="${RMATS_OUTPUT_DIR}/sample2_MXE.MATS.JC.txt"
   # ... add more samples as needed
   
   # Create a MultiPath-PSI configuration file (e.g., multipath_psi_config.yaml)
   # This file specifies the input files from other tools, sample groups, and event types.
   # Adjust paths and sample information according to your experimental design.
   cat << EOF > multipath_psi_config.yaml
   project_name: iPSC_CM_AlternativeSplicing
   output_dir: ./multipath_psi_results
   samples:
     - name: iPSC_CM_Sample1
       group: control
       rmats_se_jc: /path/to/your/rmats_output_directory/iPSC_CM_Sample1_SE.MATS.JC.txt
       rmats_mxe_jc: /path/to/your/rmats_output_directory/iPSC_CM_Sample1_MXE.MATS.JC.txt
       # Add other event types (A3SS, A5SS, RI) and tools (e.g., leafcutter_output) as needed
     - name: iPSC_CM_Sample2
       group: control
       rmats_se_jc: /path/to/your/rmats_output_directory/iPSC_CM_Sample2_SE.MATS.JC.txt
       rmats_mxe_jc: /path/to/your/rmats_output_directory/iPSC_CM_Sample2_MXE.MATS.JC.txt
     - name: iPSC_CM_Sample3
       group: treated
       rmats_se_jc: /path/to/your/rmats_output_directory/iPSC_CM_Sample3_SE.MATS.JC.txt
       rmats_mxe_jc: /path/to/your/rmats_output_directory/iPSC_CM_Sample3_MXE.MATS.JC.txt
     - name: iPSC_CM_Sample4
       group: treated
       rmats_se_jc: /path/to/your/rmats_output_directory/iPSC_CM_Sample4_SE.MATS.JC.txt
       rmats_mxe_jc: /path/to/your/rmats_output_directory/iPSC_CM_Sample4_MXE.MATS.JC.txt
   tools:
     - rmats # Specify the upstream tools whose outputs are being integrated
   event_types:
     - SE
     - MXE # Specify the alternative splicing event types to analyze
     # - A3SS
     # - A5SS
     # - RI
   EOF
   
   # Run MultiPath-PSI to integrate and quantify PSI values
   multipath-psi run --config multipath_psi_config.yaml

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3. 3

   MultiPath-PSI is available through AltAnalyze version 2.1.1, requiring aligned BAM files as input and was extensively benchmarked against other local splicing variation approaches (http://altanalyze.readthedocs.io/en/latest/Algorithms/#multipath-psi-splicing-algorithm).

   AltAnalyze v2.1.1 GitHub

   $ Bash example

   # Install AltAnalyze (if not already installed)
   # It is recommended to install AltAnalyze in a dedicated Python environment.
   # For example, using pip:
   # pip install AltAnalyze==2.1.1
   
   # Example command for running MultiPath-PSI using AltAnalyze version 2.1.1
   # Input: A directory containing aligned BAM files (e.g., /path/to/aligned_bams).
   # Output: A directory where MultiPath-PSI results will be stored (e.g., /path/to/output_multipathpsi).
   # Species: Homo sapiens (Hs) is used as a common default; replace if your data is from a different species.
   # The --runMultiPathPSI flag activates the MultiPath-PSI algorithm.
   # Ensure 'AltAnalyze.py' is accessible in your PATH or specify its full path.
   python AltAnalyze.py --run RNASeq --species Hs --platform RNASeq --input /path/to/aligned_bams --output /path/to/output_multipathpsi --runMultiPathPSI

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4. 4

   For gene expression Quantification Kallisto (https://pachterlab.github.io/kallisto/) was used.

   kallisto v0.48.0 GitHub

   $ Bash example

   # Install kallisto (example using conda)
   # conda install -c bioconda kallisto
   
   # Placeholder for Kallisto index (e.g., built from human GRCh38 transcriptome, GENCODE annotation)
   # First, download a transcriptome FASTA file (e.g., from GENCODE or Ensembl)
   # Example: wget ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_45/gencode.v45.transcripts.fa.gz
   # Then build the index:
   # kallisto index -i human_GRCh38_gencode_v45.idx gencode.v45.transcripts.fa.gz
   
   # Run kallisto quantification for paired-end reads
   # Replace 'read1.fastq.gz' and 'read2.fastq.gz' with your actual input FASTQ files
   # Replace 'human_GRCh38_gencode_v45.idx' with your actual Kallisto index file
   # Replace 'kallisto_quant_output' with your desired output directory name
   kallisto quant -i human_GRCh38_gencode_v45.idx -o kallisto_quant_output read1.fastq.gz read2.fastq.gz

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Tools Used