ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies

Jing Tang; Jianbo Fu; Yunxia Wang; Bo Li; Yinghong Li; Qingxia Yang; Xuejiao Cui; Jiajun Hong; Xiaofeng Li; Yuzong Chen; Weiwei Xue; Feng Zhu

doi:10.1093/bib/bby127

ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies

Brief Bioinform. 2020 Mar 23;21(2):621-636. doi: 10.1093/bib/bby127.

Authors

Jing Tang^{1

2}, Jianbo Fu¹, Yunxia Wang¹, Bo Li², Yinghong Li^{1

2}, Qingxia Yang^{1

2}, Xuejiao Cui^{1

2}, Jiajun Hong¹, Xiaofeng Li^{1

2}, Yuzong Chen³, Weiwei Xue², Feng Zhu^{1

2}

Affiliations

¹ College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
² School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China.
³ Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore, Singapore.

Abstract

Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.

Keywords: LFQ workflow; label-free quantification; metaproteomics; microbiota; performance assessment.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Internet
Proteins / chemistry*
Proteomics / methods*
Reproducibility of Results
Workflow*

Substances

Proteins