Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli

Ernst, J and Beg, QK and Kay, KA and Balázsi, G and Oltvai, ZN and Bar-Joseph, Z (2008) A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli. PLoS Computational Biology, 4 (3). ISSN 1553-734X

Published Version
Available under License : See the attached license file.

Download (556kB) | Preview
[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)


While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmisupervised REgulatory Network Discoverer), a semi-supervised learning method that uses a curated database of verified transcriptional factor-gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor-gene interactions, including whether the transcription factor activates or represses the gene. Using genome-wide binding datasets for several transcription factors, we demonstrate that our semi-supervised classification strategy improves the prediction of targets for a given transcription factor. To further demonstrate the utility of our inferred interactions, we generated a new microarray gene expression dataset for the aerobic to anaerobic shift response in E. coli. We used our inferred interactions with the verified interactions to reconstruct a dynamic regulatory network for this response. The network reconstructed when using our inferred interactions was better able to correctly identify known regulators and suggested additional activators and repressors as having important roles during the aerobic-anaerobic shift interface. © 2008 Ernst et al.


Social Networking:
Share |


Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Ernst, J
Beg, QK
Kay, KA
Balázsi, G
Oltvai, ZNoltvai@pitt.eduOLTVAI
Bar-Joseph, Z
ContributionContributors NameEmailPitt UsernameORCID
Date: 1 March 2008
Date Type: Publication
Journal or Publication Title: PLoS Computational Biology
Volume: 4
Number: 3
DOI or Unique Handle: 10.1371/journal.pcbi.1000044
Refereed: Yes
ISSN: 1553-734X
PubMed ID: 18369434
Date Deposited: 18 Jul 2012 20:52
Last Modified: 02 Feb 2019 14:55


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item