MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes

Mushan Li; Shiqi Tu; Zijia Li; Fengxiang Tan; Jian Liu; Qian Wang; Yuannyu Zhang; Jian Xu; Yijing Zhang; Feng Zhou; Zhen Shao

doi:10.1038/s41421-019-0107-9

MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes

Cell Discov. 2019 Aug 13:5:40. doi: 10.1038/s41421-019-0107-9. eCollection 2019.

Authors

Mushan Li^#¹, Shiqi Tu^#^{1

2}, Zijia Li³, Fengxiang Tan^{1

2}, Jian Liu⁴, Qian Wang⁵, Yuannyu Zhang^{1

6}, Jian Xu⁶, Yijing Zhang⁴, Feng Zhou⁵, Zhen Shao¹

Affiliations

¹ 1CAS Key Laboratory of Computational Biology, Collaborative Innovation Center for Genetics and Developmental Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031 China.
² 2University of Chinese Academy of Sciences, Beijing, 100049 China.
³ 3Department of Immunology and Rheumatology, Gansu Provincial Hospital, Lanzhou, 730000 China.
⁴ 4National Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
⁵ 5Institutes of Biomedical Sciences, Shanghai Medical College of Fudan University, Shanghai, 200032 China.
⁶ 6Department of Pediatrics, Children's Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390 USA.

^# Contributed equally.

Abstract

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.

Keywords: Bioinformatics; Proteomic analysis; Proteomics.