Evidence map›Paper›PMID 24688849›Full record

ArticlePeerJ2014

System wide analyses have underestimated protein abundances and the importance of transcription in mammals.

Jingyi Jessica Li, Peter J Bickel, Mark D Biggin

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 164 papers.

0numbers the graph read from it
0cells of the map it votes in
164citing papers in PubMed
17.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

164 citing papers in PubMed, 300 citations in OpenAlex.

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  9. Single-cell spatial proteomics.Histology and histopathology · 2025
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  12. Gene Signatures and Oncology Treatment Implications.Hematology/oncology clinics of North America · 2025
    Review
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  15. Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

104 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Jingyi Jessica LiDepartment of Statistics, University of California , Berkeley, CA , USA ; Departments of Statistics and Human Genetics, University of California , Los Angeles, CA , USA.
Peter J BickelDepartment of Statistics, University of California , Berkeley, CA , USA.
Mark D BigginGenomics Division, Lawrence Berkeley National Laboratory , Berkeley, CA , USA.
University of California, Berkeley · USLawrence Berkeley National Laboratory · US

Funding

Using transgenic assays to test, refine and validate the computational modelsP01GM099655 · NIGMS · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI BIGGIN, MARK D · 2012 to 2016
$9.6M
NIGMS NIH HHS P01 GM099655
6 · The paper itself

Abstract

Large scale surveys in mammalian tissue culture cells suggest that the protein expressed at the median abundance is present at 8,000-16,000 molecules per cell and that differences in mRNA expression between genes explain only 10-40% of the differences in protein levels. We find, however, that these surveys have significantly underestimated protein abundances and the relative importance of transcription. Using individual measurements for 61 housekeeping proteins to rescale whole proteome data from Schwanhausser et al. (2011), we find that the median protein detected is expressed at 170,000 molecules per cell and that our corrected protein abundance estimates show a higher correlation with mRNA abundances than do the uncorrected protein data. In addition, we estimated the impact of further errors in mRNA and protein abundances using direct experimental measurements of these errors. The resulting analysis suggests that mRNA levels explain at least 56% of the differences in protein abundance for the 4,212 genes detected by Schwanhausser et al. (2011), though because one major source of error could not be estimated the true percent contribution should be higher. We also employed a second, independent strategy to determine the contribution of mRNA levels to protein expression. We show that the variance in translation rates directly measured by ribosome profiling is only 12% of that inferred by Schwanhausser et al. (2011), and that the measured and inferred translation rates correlate poorly (R(2) = 0.13). Based on this, our second strategy suggests that mRNA levels explain ∼81% of the variance in protein levels. We also determined the percent contributions of transcription, RNA degradation, translation and protein degradation to the variance in protein abundances using both of our strategies. While the magnitudes of the two estimates vary, they both suggest that transcription plays a more important role than the earlier studies implied and translation a much smaller role. Finally, the above estimates only apply to those genes whose mRNA and protein expression was detected. Based on a detailed analysis by Hebenstreit et al. (2012), we estimate that approximately 40% of genes in a given cell within a population express no mRNA. Since there can be no translation in the absence of mRNA, we argue that differences in translation rates can play no role in determining the expression levels for the ∼40% of genes that are non-expressed.

Indexed as

Gene expressionMass spectrometryProtein abundanceTranscriptionTranslation

Identifiers

PMID24688849
PMCPMC3940484
OpenAlexW2000189769

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.