GSE175886 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

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 (example using conda)
   # conda install -c bioconda star=2.4.2a
   
   # Build STAR genome index (if not already built)
   # This step would typically be done once for a given genome/annotation combination.
   # The description implies the index for hg19 and Ensembl 72 is already available.
   # Example command to build the index (replace paths and number of threads):
   # STAR --runMode genomeGenerate \
   #      --genomeDir /path/to/STAR_hg19_Ensembl72_index \
   #      --genomeFastaFiles /path/to/hg19.fa \
   #      --sjdbGTFfile /path/to/Ensembl72.gtf \
   #      --sjdbOverhang 100 \
   #      --runThreadN 8
   
   # Align RNA-Seq data using STAR
   # Replace /path/to/STAR_hg19_Ensembl72_index with the actual path to your genome index.
   # Replace read1.fastq.gz and read2.fastq.gz with your input FASTQ files.
   # Adjust --runThreadN based on available CPU cores.
   STAR --genomeDir /path/to/STAR_hg19_Ensembl72_index \
        --readFilesIn read1.fastq.gz read2.fastq.gz \
        --runThreadN 8 \
        --outFileNamePrefix aligned_reads_ \
        --outSAMtype BAM SortedByCoordinate \
        --outSAMattributes Standard \
        --readFilesCommand zcat \
        --outFilterType BySJout \
        --outFilterMultimapNmax 20 \
        --outFilterMismatchNmax 999 \
        --outFilterMismatchNoverLmax 0.04 \
        --alignIntronMin 20 \
        --alignIntronMax 1000000 \
        --alignMatesGapMax 1000000 \
        --limitBAMsortRAM 30000000000 # Example: 30GB RAM for sorting
   

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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 v1.0.0

   $ Bash example

   # Install MultiPath-PSI
   # pip install multipath-psi
   
   # Define reference genome and annotation (using latest human assembly as placeholder)
   GENOME_FA="GRCh38.primary_assembly.genome.fa" # Placeholder for human reference genome
   GTF_FILE="gencode.v44.annotation.gtf"       # Placeholder for GENCODE annotation
   INDEX_DIR="multipath_psi_index"
   OUTPUT_DIR="multipath_psi_output"
   
   # Download reference files (example, replace with actual download commands if needed)
   # wget -c https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_44/GRCh38.primary_assembly.genome.fa.gz
   # gunzip GRCh38.primary_assembly.genome.fa.gz
   # wget -c https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_44/gencode.v44.annotation.gtf.gz
   # gunzip gencode.v44.annotation.gtf.gz
   
   # Create index for MultiPath-PSI
   # This step requires a genome FASTA and a GTF/GFF annotation file.
   mkdir -p "${INDEX_DIR}"
   multipath-psi build -g "${GENOME_FA}" -a "${GTF_FILE}" -o "${INDEX_DIR}"
   
   # Example RNA-Seq BAM files (replace with actual input files from iPSC-CM RNA-Seq)
   INPUT_BAM_1="ipsc_cm_sample1.bam"
   INPUT_BAM_2="ipsc_cm_sample2.bam"
   
   # Create output directory
   mkdir -p "${OUTPUT_DIR}"
   
   # Run MultiPath-PSI quantification
   # This command quantifies alternative splicing events (PSI values) from RNA-Seq BAM files.
   multipath-psi quant -i "${INDEX_DIR}" -o "${OUTPUT_DIR}" "${INPUT_BAM_1}" "${INPUT_BAM_2}"

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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)
   # pip install AltAnalyze
   
   # Assuming AltAnalyze.py is in your PATH or current directory
   # Create a directory for input BAM files and copy your aligned BAMs into it
   # mkdir -p /path/to/input_bams
   # cp your_aligned_file1.bam /path/to/input_bams/
   # cp your_aligned_file2.bam /path/to/input_bams/
   
   # Run AltAnalyze for splicing analysis, which includes MultiPath-PSI.
   # --species: Specify the species (e.g., Hs for Homo sapiens, Mm for Mus musculus).
   # --array_type: Specify the assay type (e.g., RNASeq).
   # --input_files: Path to a directory containing aligned BAM files.
   # --output_dir: Directory to store AltAnalyze results.
   python AltAnalyze.py \
       --run_splicing \
       --species Hs \
       --array_type RNASeq \
       --input_files /path/to/input_bams \
       --output_dir altanalyze_multipath_psi_output

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4. 4

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

   kallisto vNot specified GitHub

   $ Bash example

   # Install kallisto (example using conda)
   # conda install -c bioconda kallisto
   
   # Placeholder for kallisto index creation
   # Replace 'transcripts.fasta' with your actual transcriptome FASTA file (e.g., from Ensembl, GENCODE, RefSeq)
   # Replace 'human_transcriptome.idx' with your desired index name
   # kallisto index -i human_transcriptome.idx transcripts.fasta
   
   # Example kallisto quantification command for paired-end reads
   # Replace 'human_transcriptome.idx' with your actual kallisto index
   # Replace 'read1.fastq.gz' and 'read2.fastq.gz' with your actual input FASTQ files
   # Replace 'output_dir' with your desired output directory
   kallisto quant -i human_transcriptome.idx -o output_dir read1.fastq.gz read2.fastq.gz
   
   # For single-end reads, use the --single flag and specify fragment length and standard deviation:
   # kallisto quant -i human_transcriptome.idx -o output_dir --single -l 200 -s 20 read1.fastq.gz

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Tools Used