Comparing protein abundance and mRNA expression levels on a genomic scale
Open accessAbstract
Attempts to correlate protein abundance with mRNA expression levels have had variable success. We review the results of these comparisons, focusing on yeast. In the process, we survey experimental techniques for determining protein abundance, principally two-dimensional gel electrophoresis and mass-spectrometry. We also merge many of the available yeast protein-abundance datasets, using the resulting larger 'meta-dataset' to find correlations between protein and mRNA expression, both globally and within smaller categories.
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Noise in measurements can cause many of the observations attributed to post-transcriptional regulation.
“However, as frequently noted [1, 7, 9, 10, 19–21], noise in measurements can cause many of the observations attributed to post-transcriptional regulation.”
Studies that have attempted deliberate co-analysis of RNA and protein have consistently observed a limited correlation (between 0.5-0.7) in abundance.
“Importantly, those studies that have attempted deliberate co-analysis of RNA and protein have consistently observed a limited correlation (between 0.5–0.7) in abundance.”
From Decoding neuroproteomics: integrating the genome, translatome and functional anatomy (2014) · cites this paper as [54]
Measurements of mRNA abundance alone are insufficient to be considered predictive of protein abundance.
“As such, measurements of mRNA abundance alone are insufficient to be considered predictive of protein abundance54–56, reinforcing the requirement for cellular analysis at the proteome level.”
From Decoding neuroproteomics: integrating the genome, translatome and functional anatomy (2014) · cites this paper as [54]
The correlation between mRNA and protein levels can be weak and variable, possibly due to post-transcriptional modifications.
“Despite these developments, it has also been shown that the correlation between mRNA and protein levels can be weak and variable, possibly due to post-transcriptional modifications (Greenbaum et al., 2003; Csárdi et al., 2015).”
From Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present, and future (2018) · cites this paper as Greenbaum et al., 2003
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References 50 · Cited by 1,692
- Authors
- Greenbaum, D.
- Affiliations
- Yale University (US)
- ORCID
- D Greenbaum
- Venue
- Genome biology
- Published
- 2003
- Volume
- 4
- Issue
- 9
- Pages
- 117
- ISSN
- 1465-6906 (ISSN-L)
- PMC
- PMC193646
- OpenAlex
- W2107363136
- Cited by
- 1,692
- References
- 50
- Citations by year
- 2026: 35 · 2025: 44 · 2024: 78 · 2023: 75 · 2022: 91 · 2021: 89 · 2020: 97 · 2019: 99 · 2018: 105 · 2017: 94 · 2016: 81 · 2015: 94 · 2014: 115 · 2013: 126 · 2012: 79
- FWCI
- 15.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (n01hv28186-13-0-0)
- National Heart, Lung, and Blood Institute (N01 HV-28186, N01 HV028186)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- protein abundance, mRNA expression, proteomics, transcriptomics, yeast, Saccharomyces cerevisiae, two-dimensional gel electrophoresis, mass spectrometry, meta-dataset, global correlation
- MeSH terms
- Animals, Humans, Proteins, RNA, Messenger, Mass Spectrometry, Electrophoresis, Gel, Two-Dimensional, Gene Expression, Genomics, Proteomics
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