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

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number40
JournalCell Discovery
Volume5
Issue number1
DOIs
StatePublished - Dec 1 2019

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ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Cell Biology

Cite this

Li, M., Tu, S., Li, Z., Tan, F., Liu, J., Wang, Q., Zhang, Y., Xu, J., Zhang, Y., Zhou, F., & Shao, Z. (2019). MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes. Cell Discovery, 5(1), [40]. https://doi.org/10.1038/s41421-019-0107-9