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

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 journal › Article › peer-review

9 Scopus citations

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 language	English (US)
Article number	40
Journal	Cell Discovery
Volume	5
Issue number	1
DOIs	https://doi.org/10.1038/s41421-019-0107-9
State	Published - Dec 1 2019

ASJC Scopus subject areas

Biochemistry
Molecular Biology
Genetics
Cell Biology

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

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.",

author = "Mushan Li and Shiqi Tu and Zijia Li and Fengxiang Tan and Jian Liu and Qian Wang and Yuannyu Zhang and Jian Xu and Yijing Zhang and Feng Zhou and Zhen Shao",

note = "Funding Information: We thank Dr. Guo-cheng Yuan in Dana Farber Cancer Institute for helpful discussion and suggestions. We also thank the reviewers for their comments, which have greatly helped to improve our analysis and manuscript. This study was supported by the National Basic Research Program of China (2018YFA0107602), the “100-Talent Program” (Y516C11851) of Chinese Academy of Science, and the National Natural Science Foundation of China (31871280 and 31701140). Publisher Copyright: {\textcopyright} 2019, The Author(s).",

year = "2019",

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doi = "10.1038/s41421-019-0107-9",

language = "English (US)",

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T2 - model-based analysis of proteomic data to detect proteins with significant abundance changes

AU - Li, Mushan

AU - Tu, Shiqi

AU - Li, Zijia

AU - Tan, Fengxiang

AU - Liu, Jian

AU - Wang, Qian

AU - Zhang, Yuannyu

AU - Xu, Jian

AU - Zhang, Yijing

AU - Zhou, Feng

AU - Shao, Zhen

N1 - Funding Information: We thank Dr. Guo-cheng Yuan in Dana Farber Cancer Institute for helpful discussion and suggestions. We also thank the reviewers for their comments, which have greatly helped to improve our analysis and manuscript. This study was supported by the National Basic Research Program of China (2018YFA0107602), the “100-Talent Program” (Y516C11851) of Chinese Academy of Science, and the National Natural Science Foundation of China (31871280 and 31701140). Publisher Copyright: © 2019, The Author(s).

PY - 2019/12/1

Y1 - 2019/12/1

N2 - 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.

AB - 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.

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

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