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Estimating a structured covariance matrix from multi-lab measurements in high-throughput biology
Alexander M Franks1, Gábor Csárdi1, D Allan Drummond2
1Department of Statistics at Harvard University.
Journal of the American Statistical Association
|May 9, 2015
Summary
Researchers found high coordination between mRNA and protein levels in yeast, challenging previous findings of poor regulation. This suggests post-transcriptional regulation is less significant than once believed.
Area of Science:
- Molecular Biology
- Systems Biology
- Biostatistics
Background:
- Previous studies reported a surprising lack of coordination between transcription and translation in various organisms.
- These prior findings may stem from methodological limitations, including failure to control for biases and measurement errors.
Purpose of the Study:
- To re-evaluate the coordination between mRNA and protein levels in yeast using a rigorous meta-analysis.
- To develop a more accurate estimate of the correlation between mRNA and protein levels, serving as a proxy for gene expression coordination.
Main Methods:
- Conducted a meta-analysis of 27 yeast datasets.
- Employed a multilevel model with full uncertainty quantification.
- Incorporated sensitivity analyses and novel statistical theory to address noise and model mis-specifications.
Main Results:
- The correlation between mRNA and protein levels in yeast was found to be significantly high.
- This high correlation suggests a greater degree of coordination between transcription and translation than previously reported.
Conclusions:
- The coordination between mRNA and protein levels is substantial in yeast.
- Post-transcriptional regulation appears to play a less dominant role in gene expression control than previously hypothesized.
