Isotope-labeling-based mass spectrometry (MS) is widely used in quantitative proteomic studies. With this technique, the relative abundance of thousands of proteins can be efficiently profiled in parallel, greatly facilitating the detection of proteins differentially expressed across samples. However, this task remains computationally challenging. Here we present a new approach, termed Model-based Analysis of Proteomic data (MAP), for this task. Unlike many existing methods, MAP does not require technical replicates to model technical and systematic errors, and instead utilizes a novel step-by-step regression analysis to directly assess the significance of observed protein abundance changes. We applied MAP to compare the proteomic profiles of undifferentiated and differentiated mouse embryonic stem cells (mESCs), and found it has superior performance compared with existing tools in detecting proteins differentially expressed during mESC differentiation. A web-based application of MAP is provided for online data processing at http://bioinfo.sibs.ac.cn/shaolab/MAP.
[1]Chinese Acad Sci, Shanghai Inst Biol Sci, Shanghai Inst Nutr & Hlth,CAS Key Lab Computat Bi, Collaborat Innovat Ctr Genet & Dev Biol,CAS MPG P, Shanghai 200031, Peoples R China;
[2]Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
[3]Gansu Prov Hosp, Dept Immunol & Rheumatol, Lanzhou 730000, Gansu, Peoples R China;
[4]Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Plant Physiol & Ecol, Natl Lab Plant Mol Genet,CAS Ctr Excellence Mol P, Shanghai, Peoples R China;
[5]Fudan Univ, Inst Biomed Sci, Shanghai Med Coll, Shanghai 200032, Peoples R China;
[6]Univ Texas Southwestern Med Ctr Dallas, Childrens Med Ctr, Dept Pediat, Res Inst, Dallas, TX 75390 USA
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