GSE69889 Processing Pipeline

Publication

Genes & development (2020) — PMID 32616519

Dataset

Genome-wide Circadian Control of Transcription at Active Enhancers Regulates Insulin Secretion and Diabetes Risk

1. 1

   The data was analyzed by RNA Express v1.0 in BaseSpace

   RNA Express v1.0

   $ Bash example

   # The analysis was performed using "RNA Express v1.0" within the Illumina BaseSpace platform.
   # This is an application typically run via a graphical user interface or API, not a direct command-line tool.
   # Specific command-line parameters and an executable bash command cannot be generated from the provided description.
   
   # Placeholder for reference genome, commonly used in RNA analysis:
   # REF_GENOME="GRCh38"
   # REF_GTF="gencode.v38.annotation.gtf"

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2. 2

   Reads were aligned to mm10 using STAR with the following parameters: readFilesCommand zcat outFilterType BySJout outSJfilterCountUniqueMin -1 2 2 2 outSJfilterCountTotalMin -1 2 2 2 outFilterIntronMotifs RemoveNoncanonical genomeLoad LoadAndKeep

   STAR v2.7.10a (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install STAR (example using conda)
   # conda install -c bioconda star
   
   # Create a genome directory (if not already present)
   # mkdir -p /path/to/STAR_genome_index/mm10
   
   # Build STAR genome index (if not already present, using mm10 as reference)
   # This step is usually done once per genome assembly
   # STAR --runMode genomeGenerate \
   #      --genomeDir /path/to/STAR_genome_index/mm10 \
   #      --genomeFastaFiles /path/to/mm10.fa \
   #      --sjdbGTFfile /path/to/mm10.gtf \
   #      --runThreadN <number_of_threads>
   
   # Align reads using STAR
   STAR --genomeDir /path/to/STAR_genome_index/mm10 \
        --readFilesIn input_reads.fastq.gz \
        --readFilesCommand zcat \
        --outFileNamePrefix output_aligned_prefix_ \
        --outFilterType BySJout \
        --outSJfilterCountUniqueMin -1 2 2 2 \
        --outSJfilterCountTotalMin -1 2 2 2 \
        --outFilterIntronMotifs RemoveNoncanonical \
        --genomeLoad LoadAndKeep \
        --outSAMtype BAM SortedByCoordinate \
        --outSAMunmapped Within \
        --outSAMattributes Standard \
        --runThreadN <number_of_threads>

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3. 3

   Gene expression levels were computed by a proprietary alogorithm in BaseSpace

   Proprietary algorithm in BaseSpace (Inferred with models/gemini-2.5-flash) vN/A GitHub

   $ Bash example

   # Gene expression levels were computed using a proprietary algorithm within Illumina BaseSpace.
   # Specific tool and parameters are not disclosed.
   # Placeholder for a generic gene expression quantification step, assuming human GRCh38 reference:
   # proprietary_expression_tool --input_reads reads.fastq.gz --reference_genome GRCh38.fa --annotation_gtf GRCh38.gtf --output_expression_matrix expression_levels.tsv

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4. 4

   Differential expression was performed with DESeq2 using default parameters

   DESeq2 v1.44.0 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install R and Bioconductor if not already present
   # 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")'
   
   # Create dummy count data (replace with your actual counts.tsv)
   cat 'gene_id\tsampleA\tsampleB\tsampleC\tsampleD
   gene1\t100\t120\t50\t60
   gene2\t50\t60\t100\t110
   gene3\t200\t210\t180\t190
   gene4\t10\t12\t20\t22' > counts.tsv
   
   # Create dummy sample information (replace with your actual sample_info.tsv)
   cat 'sample\tcondition
   sampleA\tcontrol
   sampleB\tcontrol
   sampleC\ttreated
   sampleD\ttreated' > sample_info.tsv
   
   # Create an R script for DESeq2 analysis
   cat << 'EOF' > run_deseq2.R
   library(DESeq2)
   
   # Load count data
   count_data <- read.table("counts.tsv", header = TRUE, row.names = 1, sep = "\t")
   
   # Load sample information
   sample_info <- read.table("sample_info.tsv", header = TRUE, row.names = 1, sep = "\t")
   
   # Ensure sample order matches count data columns
   sample_info <- sample_info[colnames(count_data), , drop = FALSE]
   
   # Create DESeqDataSet object
   dds <- DESeqDataSetFromMatrix(countData = count_data,
                                 colData = sample_info,
                                 design = ~ condition)
   
   # Run DESeq2 analysis with default parameters
   dds <- DESeq(dds)
   
   # Get results (e.g., comparing 'treated' vs 'control')
   res <- results(dds, contrast = c("condition", "treated", "control"))
   
   # Order results by adjusted p-value
   res <- res[order(res$padj), ]
   
   # Write results to a CSV file
   write.csv(as.data.frame(res), file = "deseq2_results.csv")
   
   message("DESeq2 analysis complete. Results saved to deseq2_results.csv")
   EOF
   
   # Execute the R script
   Rscript run_deseq2.R

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5. 5

   Gene cycling data was computed with eJTK_Cycle

   eJTK_Cycle vNot specified

   $ Bash example

   # Clone the eJTK_Cycle repository if not already present
   # git clone https://github.com/alan-yeo/eJTK_Cycle.git
   # cd eJTK_Cycle
   
   # Run eJTK_Cycle to compute gene cycling data.
   # This command assumes 'gene_expression_data.tsv' is the input file containing gene expression values
   # (e.g., genes as rows, time points as columns).
   # 'cycling_results.tsv' will be the output file with cycling analysis results.
   # -p: Specifies the period to test for (e.g., 24 for circadian rhythms).
   # -a: Specifies the alpha (significance) level for p-value correction (e.g., 0.05).
   python eJTK_Cycle.py -i gene_expression_data.tsv -o cycling_results.tsv -p 24 -a 0.05

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Tools Used