proteiNorm – A User-Friendly Tool for Normalization and Analysis of TMT and Label-Free Protein Quantification
Open accessAbstract
The technological advances in mass spectrometry allow us to collect more comprehensive data with higher quality and increasing speed. With the rapidly increasing amount of data generated, the need for streamlining analyses becomes more apparent. Proteomics data is known to be often affected by systemic bias from unknown sources, and failing to adequately normalize the data can lead to erroneous conclusions. To allow researchers to easily evaluate and compare different normalization methods via a user-friendly interface, we have developed “proteiNorm”. The current implementation of proteiNorm accommodates preliminary filters on peptide and sample levels followed by an evaluation of several popular normalization methods and visualization of the missing value. The user then selects an adequate normalization method and one of the several imputation methods used for the subsequent comparison of different differential expression methods and estimation of statistical power. The application of proteiNorm and interpretation of its results are demonstrated on two tandem mass tag multiplex (TMT6plex and TMT10plex) and one label-free spike-in mass spectrometry example data set. The three data sets reveal how the normalization methods perform differently on different experimental designs and the need for evaluation of normalization methods for each mass spectrometry experiment. With proteiNorm, we provide a user-friendly tool to identify an adequate normalization method and to select an appropriate method for differential expression analysis.
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Quality control and normalization of the proteomics data was carried out with proteiNorm.
“Quality control (QC) and normalization was carried out with proteiNorm 61 .”
From APOE4-drives brain bioenergetic shifts independent of Alzheimer’s disease pathology (2026) · cites this paper as [61]
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Perspectives for better batch effect correction in mass-spectrometry-based proteomics
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APOE4-drives brain bioenergetic shifts independent of Alzheimer’s disease pathology
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- Authors
- Graw, S., Tang, J., Zafar, M. K., Byrd, A. K., Bolden, C., Peterson, E. C., & Byrum, S. D.
- Affiliations
- Arkansas Children's Research Institute (US)
- University of Arkansas for Medical Sciences (US)
- University of Arkansas for Medical Sciences , , , ,
- Corresponding author
- Byrum, Stephanie D
- Venue
- ACS Omega
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-09-30
- Volume
- 5
- Issue
- 40
- Pages
- 25625-25633
- ISSN
- 2470-1343 (ISSN-L)
- PMC
- PMC7557219
- OpenAlex
- W3090327832
- Cited by
- 157
- References
- 2
- Citations by year
- 2026: 46 · 2025: 35 · 2024: 26 · 2023: 21 · 2022: 17 · 2021: 12
- FWCI
- 4.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- ACS Omega (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Drug Abuse (R21DA041822)
- National Institute of General Medical Sciences (P20GM121293)
- Arkansas Biosciences Institute (NA)
- University of Arkansas for Medical Sciences (NA)
- Arkansas Children’s Hospital Research Institute (NA)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Keywords
- tandem mass tags, label-free quantification, normalization method comparison, missing data imputation, differential expression analysis, statistical power estimation
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