Identification of dominant signaling pathways from proteomics expression data.
The availability of the results of high-throughput analyses coming from 'omic' technologies has been one of the major driving forces of pathway biology. Analytical pathway biology strives to design a 'pathway search engine', where the input is the 'omic' data and the output is the list of activated or dominant pathways in a given sample. Here we describe the first attempt to design and validate such a pathway search engine using as input expression proteomics data. The engine represents a specific workflow in computational tools developed originally for mRNA analysis (BMC Bioinformatics 2006, 7 (Suppl 2), S13). Using our own datasets as well as data from recent proteomics literature we demonstrate that different dominant pathways (EGF, TGF(beta), stress, and Fas pathways) can be correctly identified even from limited datasets. Pathway search engines can find application in a variety of proteomics-related fields, from fundamental molecular biology to search for novel types of disease biomarkers.
Zubarev
Roman A
RA
Division of Molecular Biometry, Institute for Cell and Molecular Biology, Uppsala University, SE-75 124 Uppsala, Sweden. Roman.Zubarev@icm.uu.se
Nielsen
Michael L
ML
Fung
Eva M
EM
Savitski
Mikhail M
MM
Kel-Margoulis
Olga
O
Wingender
Edgar
E
Kel
Alexander
A
D000818:Animals; D045744:Cell Line, Tumor; D002478:Cells, Cultured; D019295:Computational Biology; D004815:Epidermal Growth Factor / Q000378:metabolism; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D008907:Mink; D020543:Proteome / Q000378:metabolism; D040901:Proteomics; D015203:Reproducibility of Results; D015398:Signal Transduction; D013601:T-Lymphocytes / Q000378:metabolism; D016212:Transforming Growth Factor beta / Q000378:metabolism; D019014:fas Receptor / Q000378:metabolism
C491901:FAS protein, human; D020543:Proteome; D016212:Transforming Growth Factor beta; D019014:fas Receptor; D004815:Epidermal Growth Factor