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Mol Cell Proteomics. 2019 May 16. pii: mcp.RA118.001169. doi: 10.1074/mcp.RA118.001169. [Epub ahead of print]

Simultaneous Improvement in the Precision, Accuracy and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains.

Author information

1
Zhejiang University, China.
2
Chongqing University, China.
3
National University of Singapore, Singapore.
4
Mayo Clinic, United States.
5
College of Pharmaceutical Sciences, Zhejiang University, China zhufeng@zju.edu.cn.

Abstract

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performances. However, the evaluation results using different criteria (precision, accuracy and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performances from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy & robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass spectrometry-based LFQ technique.

KEYWORDS:

Bioinformatics; Bioinformatics software; Clinical proteomics; LFQ workflow; Label-free proteome quantification; Label-free quantification; Omics; Online tool; Processing chain; Quantification tool; SWATH-MS

PMID:
31097671
DOI:
10.1074/mcp.RA118.001169
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