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LFAQ: towards unbiased label-free absolute protein quantification by predicting peptide quantitative factors
Open accessPreprint — published as 10.1021/acs.analchem.8b03267
Abstract
Mass spectrometry (MS) has become a prominent choice for large-scale absolute protein quantification, but its quantification accuracy still has substantial room for improvement. A crucial issue is the bias between the peptide MS intensity and the actual peptide abundance, i.e., the fact that peptides with equal abundance may have different MS intensities. This bias is mainly caused by the diverse physicochemical properties of peptides. Here, we propose a novel algorithm for label-free absolute protein quantification, LFAQ, which can correct the biased MS intensities by using the predicted peptide quantitative factors for all identified peptides. When validated on datasets produced by different MS instruments and data acquisition modes, LFAQ presented accuracy and precision superior to those of existing methods. In particular, it reduced the quantification error by an average of 46% for low-abundance proteins.
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Cites
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“The MS intensity bias is hard to model and often ignored by absolute quantification algorithms 12, 13 .”
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Metadata
- Authors
- Chang, C., Gao, Z., Ying, W., Zhao, Y., Fu, Y., Wu, S., Li, M., Wang, G., Qian, X., Zhu, Y., & He, F.
- Affiliations
- Beijing Proteome Research Center (CN)
- Chinese Academy of Sciences (CN)
- Anhui Medical University (CN)
- Corresponding author
- Fu, Yan
- Qian, Xiaohong
- Zhu, Yunping
- He, Fuchu
- ORCID
- Venue
- Analytical Chemistry
- Publisher
- openRxiv
- Published
- 2018-05-23
- Volume
- 91
- Issue
- 2
- Pages
- 1335-1343
- ISSN
- 0003-2700 (ISSN-L)
- OpenAlex
- W2953367041
- Cited by
- 12
- References
- 1
- Citations by year
- 2026: 2 · 2023: 3 · 2022: 1 · 2021: 1 · 2020: 4 · 2019: 1
- FWCI
- 0.65
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Analytical Chemistry (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (XDB13040600, 2017YFC0906602, 21605159, 2014DFB30010, 2017YFA0505002, 21475150, 16CXZ027, 2015AA020108)
- Chinese Academy of Sciences (XDB13040600)
- Ministry of Science and Technology of the People's Republic of China (2014DFB30010, 2015AA020108, 2017YFC0906600, 2016YFA0501300, 2017YFA0505002, 2014CBA02001)
- National Key Research and Development Program of China (2017YFA0505002, 2017YFC0906602, 2016YFA0501300, 16CXZ027, 2014CBA02001, 2015AA020108)
- National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences (XDB13040600)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Keywords
- absolute protein quantification, label-free quantification, peptide abundance, low-abundance proteins
- MeSH terms
- RAW 264.7 Cells, Algorithms, Animals, Chromatography, Liquid, Humans, Peptides, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins, Mice, Tandem Mass Spectrometry, HEK293 Cells
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