This is a deprecated site

We recommend you use http://degust.erc.monash.edu/

Upload

Upload a CSV or tab-separated file of your RNA-Seq data. One row per gene, and one column of read counts per replicate.

Upload your CSV file. See below for the format description.

CSV File Format

You may upload a CSV of read counts per gene OR a CSV of pre-analysed gene data.

Uploading read counts per gene

After uploading your counts file, you'll be directed to a page to specify the columns. Ensure the checkbox Analyze server side is checked.

The requirements for the CSV file:

  • Must be in CSV (or tab-separated) format
  • Must have a single header row defining with a unique name for each column
  • Must have 2 or more replicates per condition, and 2 or more conditions
  • May optionally have information columns to be displayed in the gene table
  • May optionally have an EC Number column to display genes on Kegg pathways
Example CSV File
            Gene ID, name, control rep1, control rep2, treatment A rep1, treatment A rep2, EC Number
            gene001, flavodoxin, 60, 40, 200, 220, 3.1.-.-
            gene002, p53, 0, 4, 20, 30,
            gene003, potassium uptake protein, 600, 633, 200, 220, 2.7.8.-
          
Uploading pre-analysed data

After uploading your analysis file, you'll be directed to a page to specify the columns. Ensure the checkbox Analyze server side is not checked.

The requirements for CSV file:

  • Must be in CSV (or tab-separated) format
  • Must have a single header row defining with a unique name for each column
  • Must have 1 or more columns for log-fold-change
  • Must have 1 column for False Discovery Rate (or an equivalent)
  • Must have 1 column for log average expression (for the 'A' in an MA plot)
  • May optionally have information columns to be displayed in the gene table
  • May optionally have an EC Number column to display genes on Kegg pathways
Example CSV File
            Gene ID, name, treatment log-fold-change, FDR, log average expression, EC Number
            gene001, flavodoxin, 0.1, 0.65, 8.23, 3.1.-.-
            gene002, p53, -1.5, 0.0001, 10.4,
            gene003, potassium uptake protein, -1.2, 0.023, 5.32, 2.7.8.-
          

One possible way to produce such a CSV file is by performing your differential analysis using R with LIMMA and saving the results as follows:

            > # Save our analysis object 'efit' to a CSV file
            > class(efit)
            [1] "MArrayLM"
            attr(,"package")
            [1] "limma"
            > colnames(efit)
            [1] "GppX" "luxS" "cdhR"
            > write.csv(topTable(efit, number=Inf), 'dge.csv', row.names=F)
            > quit()
            bash$