1. Extracting the confidence probability of phosphorylation sites at peptide level from identification results searched by Mascot.
2. Generating the quality control file of phosphorylation sites based on score of sites from Mascot.
3. Pre-processing phosphoproteomic data.
4. Kinase activity prediction.
5. Motif enrichment analysis.
6. Data visualization

graphics, grDevices, stats, utils, stringr, ggseqlogo, samr, limma, e1071, ClueR, Rtsne, glmnet, yaml, impute

• ksea (https://github.com/evocellnet/ksea)

install.packages('devtools')
require(devtools)
install_github('evocellnet/ksea')

• rmotifx (https://github.com/omarwagih/rmotifx)

install.packages('devtools')
require(devtools)
install_github('omarwagih/rmotifx')

install.packages('devtools')
require(devtools)
install_github('ecnuzdd/PhosMap')

• Tutorial of PhosMap (Tutorial_of_PhosMap.pdf)
• Functions manual of PhosMap (Manual_of_PhosMap.pdf)
• The help for installation (lnstallation_help.pdf)

You can go to this directory to find the files needed for the case study.

ftp://111.198.139.72:4000/PhosMap/data/

Or download them directly through the following links.

This .zip contains PhosMap's demo code and raw datasets. After unzip, start the R program with setting the new folder as your working directory, and run the R script in it.

ftp://111.198.139.72:4000/PhosMap/data/PhosMap_Demo_With_BRAFi_Data.zip

The .RData file stores the input objects required to invoke functions in the PhosMap script demo, which can be downloaded from the following link.

ftp://111.198.139.72:4000/PhosMap/data/BRAFi.RData

After downloading, put it into your working directory. Use the following command to introduce these objects into the global environment, then you can smoothly execute the statements in the script.

load(BRAFi.RData)

PhosMap_datasets https://github.com/ecnuzdd/PhosMap_datasets

• data (Demo data: BRAFi.RData)
• fasta_library
  • Refseq (Human, Mouse, Rattus)
  • Uniprot (Human, Mouse, Rattus)
• id_coversion_table
  • Human, Mouse, Rattus
• kinase_substrate_regulation_relationship_table
  • Human, Mouse, Rattus
• motif_library
  • Refseq (Human, Mouse, Rattus)
  • Uniprot (Human, Mouse, Rattus)

PXD007740

ftp://111.198.139.72:4000/PhosMap/PhosMap_Demo_With_BRAFi_Data.zip

1. Ressa, A., et al. (2018) A System-wide Approach to Monitor Responses to Syner-gistic BRAF and EGFR Inhibition in Colorectal Cancer Cells, Molecular & cellular proteomics : MCP, 17, 1892-1908.
2. Feng, J., et al. (2017) Firmiana: towards a one-stop proteomic cloud platform for data processing and analysis, Nature biotechnology, 35, 409-412.