Label-free proteome quantification and evaluation. Issue 1 (19th November 2022)

• Record Type: Journal Article
• Authors: Fu, Jianbo; Yang, Qingxia; Luo, Yongchao; Zhang, Song; Tang, Jing; Zhang, Ying; Zhang, Hongning; Xu, Hanxiang; Zhu, Feng
• Is Part Of: Briefings in bioinformatics. Volume 24:Issue 1(2023)
• Publication Date: 2022-11-19
• Subjects: label-free quantification -- proteomics -- comprehensive evaluation -- well-performing chains -- R package; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac477
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: The label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility. Due to the extreme difficulty lying in an in-depth quantification, the LFQ chains incorporating a variety of transformation, pretreatment and imputation methods are required and constructed. However, it remains challenging to determine the well-performing chain, owing to its strong dependence on the studied data and the diverse possibility of integrated chains. In this study, an R package EVALFQ was therefore constructed to enable a performance evaluation on >3000 LFQ chains. This package is unique in (a) automatically evaluating the performance using multiple criteria, (b) exploring the quantification accuracy based on spiking proteins and (c) discovering the well-performing chains by comprehensive assessment. All in all, because of its superiority in assessing from multiple perspectives and scanning among over 3000 chains, this package is expected to attract broad interests from the fields of proteomic quantification. The package is available at https://github.com/idrblab/EVALFQ . Graphical abstract: