A simple metric of promoter architecture robustly predicts expression breadth of human genes suggesting that most transcription factors are positive regulators

Hurst, Laurence D., Sachenkova, Oxana, Daub, Carsten, Forrest, Alistair R. R., the FANTOM consortium, Huminiecki, Lukasz, Beckhouse, Anthony, Wells, Christine, Vijayan, Dipti, Wolvetang, Ernst, Hitchens, Kelly, Kenna, Tony, Blumenthal, Antje, Briggs, James, Ovchinnikov, Dmitry, Fearnley, Liam, Le Cao, Kim-Anh, Mason, Elizabeth and Nielsen, Lars (2014) A simple metric of promoter architecture robustly predicts expression breadth of human genes suggesting that most transcription factors are positive regulators. Genome Biology, 15 7: . doi:10.1186/s13059-014-0413-3

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author Hurst, Laurence D.
Sachenkova, Oxana
Daub, Carsten
Forrest, Alistair R. R.
the FANTOM consortium
Huminiecki, Lukasz
Beckhouse, Anthony
Wells, Christine
Vijayan, Dipti
Wolvetang, Ernst
Hitchens, Kelly
Kenna, Tony
Blumenthal, Antje
Briggs, James
Ovchinnikov, Dmitry
Fearnley, Liam
Le Cao, Kim-Anh
Mason, Elizabeth
Nielsen, Lars
Title A simple metric of promoter architecture robustly predicts expression breadth of human genes suggesting that most transcription factors are positive regulators
Journal name Genome Biology   Check publisher's open access policy
ISSN 1474-7596
1474-760X
Publication date 2014-01-01
Year available 2014
Sub-type Article (original research)
DOI 10.1186/s13059-014-0413-3
Open Access Status DOI
Volume 15
Issue 7
Total pages 26
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Formatted abstract
Background
Conventional wisdom holds that, owing to the dominance of features such as chromatin level control, the expression of a gene cannot be readily predicted from knowledge of promoter architecture. This is reflected, for example, in a weak or absent correlation between promoter divergence and expression divergence between paralogs. However, an inability to predict may reflect an inability to accurately measure or employment of the wrong parameters. Here we address this issue through integration of two exceptional resources: ENCODE data on transcription factor binding and the FANTOM5 high-resolution expression atlas.

Results
Consistent with the notion that in eukaryotes most transcription factors are activating, the number of transcription factors binding a promoter is a strong predictor of expression breadth. In addition, evolutionarily young duplicates have fewer transcription factor binders and narrower expression. Nonetheless, we find several binders and cooperative sets that are disproportionately associated with broad expression, indicating that models more complex than simple correlations should hold more predictive power. Indeed, a machine learning approach improves fit to the data compared with a simple correlation. Machine learning could at best moderately predict tissue of expression of tissue specific genes.

Conclusions
We find robust evidence that some expression parameters and paralog expression divergence are strongly predictable with knowledge of transcription factor binding repertoire. While some cooperative complexes can be identified, consistent with the notion that most eukaryotic transcription factors are activating, a simple predictor, the number of binding transcription factors found on a promoter, is a robust predictor of expression breadth.
Keyword Factor Binding Sites
Human Genome
Duplicate Genes
Divergence
Mouse
Yeast
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status Non-UQ

 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 7 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 8 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Wed, 25 Mar 2015, 19:42:42 EST by Kylie Hengst on behalf of UQ Diamantina Institute