Phosphorylation enrichment

This tool analyses enrichment of biological terms from phosphoproteomics datasets taking into consideration the number of phosphoryaltion sites. Upload two datasets (e.g. foreground and background) for analysis.

Here enrichment is calcualted using Annotations from DAVID. To use annotations updated monthly (but limited to GO-terms) click here



How to

For a more in-depth "How to" please see refrence 1

  1. Determine a foreground and background phosphoproteomics dataset of interest (see refrence 2)
  2. Use DAVID ”Functional Annotation” tool to get a ’Functional Annotation table for each. Name them FwDAVID and BgDAVID respectively.
  3. Upload your foreground and background phoshproteomics datasets and DAVID output files above. The phosphoproteomics data should be in tab delimited.
    • Format: [ID] [Tab] [Site]
    • Example: P23443 T233
    • Please Note: The IDs has to be the same as the ones sumbitted to DAVID

Advantages of this Tool

This tool has two advantages over traditional Enrichment tools

First and foremost it takes site information into account, which is an advantage because it increases the statistical confidence as the number of sites is greater than or equal to the number of proteins.

Furthermore, in this tool we use an FDR (false discovery rate) based approach to correct for the size of the dataset. Most approaches for corrections for multiple testing, such as Benjamini-Hochberg and Holm-Bonferroni, are not optimal for dataset that is correlated. For example, a protein that is annotated with the GO term "positive response to insulin signaling" will by definiton also be annotated with "response to insulin signaling". This tool therefore uses an FDR approach by drawing 500 random samples from the combined foreground and background datasets, and using the distributions of these "scrambled datasets" to estimate the FDR.


  1. Munk S et al (2016) Systems Analysis for Interpretation of Phosphoproteomics Data Analysis. Metods Mol Biol 1355:341-60
    • Describes this protocol
  2. Refsgaard J et al (2016) Search Databases and Statistics: Pitfalls and Best Practices in Phosphoproteomics. Metods Mol Biol 1355:323-39
    • How to select a Forground and Background
  3. Dennis G Jr et al (2003) DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 4(5):P3
    • Describes DAVID