GSE123881 Processing Pipeline

Publication

Life science alliance (2020) — PMID 32817263

Dataset

DDX5 promotes oncogene C3 and FABP1 expressions and drives intestinal inflammation and tumorigenesis

1. 1

   Sequenced reads were mapped to mm10 using STAR v2.6.1a with parameters "--outFilterMultimapNmax 20 --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 --outFilterMismatchNmax 999 --outFilterMismatchNoverReadLmax 0.04 --alignIntronMin 20 --alignIntronMax 1000000 --alignMatesGapMax 1000000".

   STAR v2.6.1a GitHub

   $ Bash example

   # Install STAR (if not already installed)
   # conda install -c bioconda star=2.6.1a
   
   # Create STAR genome index (if not already created)
   # STAR --runThreadN 8 --runMode genomeGenerate --genomeDir /path/to/STAR_index/mm10 --genomeFastaFiles /path/to/mm10.fa --sjdbGTFfile /path/to/mm10.gtf
   
   # Align sequenced reads to mm10 using STAR
   STAR --genomeDir /path/to/STAR_index/mm10 \
        --readFilesIn input_R1.fastq.gz input_R2.fastq.gz \
        --outFileNamePrefix aligned_reads_ \
        --outSAMtype BAM SortedByCoordinate \
        --runThreadN 8 \
        --outFilterMultimapNmax 20 \
        --alignSJoverhangMin 8 \
        --alignSJDBoverhangMin 1 \
        --outFilterMismatchNmax 999 \
        --outFilterMismatchNoverReadLmax 0.04 \
        --alignIntronMin 20 \
        --alignIntronMax 1000000 \
        --alignMatesGapMax 1000000

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2. 2

   Uniquely mapped reads overlapping with the exons were counted using featureCounts for each gene in GENCODE.vM19 annotation.

   featureCounts v2.0.6

   $ Bash example

   # Install featureCounts (part of Subread package)
   # conda install -c bioconda subread
   
   # Download GENCODE vM19 annotation GTF
   # wget ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M19/gencode.vM19.annotation.gtf.gz
   # gunzip gencode.vM19.annotation.gtf.gz
   
   # Define variables
   GTF_FILE="gencode.vM19.annotation.gtf"
   INPUT_BAM="aligned_reads.bam" # Placeholder for uniquely mapped and sorted BAM file
   OUTPUT_FILE="gene_counts.txt"
   NUM_THREADS=8 # Adjust as needed
   
   # Run featureCounts
   featureCounts -a "${GTF_FILE}" \
                 -o "${OUTPUT_FILE}" \
                 -F GTF \
                 -t exon \
                 -g gene_id \
                 --primary \
                 -s 0 \
                 -T "${NUM_THREADS}" \
                 "${INPUT_BAM}"

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3. 3

   Differential expression analysis were performed by DESeq2(v1.18.1) package

   DESeq2 v1.18.1 GitHub

   $ Bash example

   #!/bin/bash
   
   # Install R and Bioconductor if not already installed
   # sudo apt-get update
   # sudo apt-get install -y r-base
   # R -e 'if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")'
   # R -e 'BiocManager::install("DESeq2", version = "3.8")' # DESeq2 v1.18.1 corresponds to Bioconductor 3.8
   
   # Create dummy count data (replace with your actual count matrix)
   echo -e "gene\tsample1\tsample2\tsample3\tsample4" > counts.tsv
   echo -e "geneA\t100\t120\t50\t60" >> counts.tsv
   echo -e "geneB\t20\t25\t80\t90" >> counts.tsv
   echo -e "geneC\t500\t550\t200\t220" >> counts.tsv
   echo -e "geneD\t10\t15\t5\t8" >> counts.tsv
   
   # Create dummy sample information (replace with your actual sample metadata)
   echo -e "sample\tcondition" > sample_info.tsv
   echo -e "sample1\tcontrol" >> sample_info.tsv
   echo -e "sample2\tcontrol" >> sample_info.tsv
   echo -e "sample3\ttreated" >> sample_info.tsv
   echo -e "sample4\ttreated" >> sample_info.tsv
   
   # Run DESeq2 analysis using R
   Rscript -e '
     library(DESeq2)
   
     # Load count data
     # Ensure the first column is gene names and subsequent columns are sample counts
     count_data <- read.table("counts.tsv", header = TRUE, row.names = 1)
   
     # Load sample information
     # Ensure the first column is sample names and subsequent columns are metadata
     sample_info <- read.table("sample_info.tsv", header = TRUE, row.names = 1)
   
     # Ensure sample order in colData matches countData columns
     sample_info <- sample_info[colnames(count_data), , drop = FALSE]
   
     # Create DESeqDataSet object
     # Replace `~ condition` with your actual design formula based on `sample_info` columns
     dds <- DESeqDataSetFromMatrix(countData = count_data,
                                   colData = sample_info,
                                   design = ~ condition)
   
     # Run DESeq2 analysis
     dds <- DESeq(dds)
   
     # Get results
     # You can specify contrast, alpha, etc. here, e.g., results(dds, contrast=c("condition","treated","control"))
     res <- results(dds)
   
     # Save results to a TSV file
     write.table(as.data.frame(res), file = "deseq2_results.tsv", sep = "\t", quote = FALSE, row.names = TRUE)
   
     message("DESeq2 analysis complete. Results saved to deseq2_results.tsv")
   '

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Tools Used