Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks

Tools
- Tools
+ Tools

Penfold, Christopher A., Buchanan-Wollaston, Vicky, Denby, Katherine J. and Wild, David L. (2012) Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks. Bioinformatics, Vol.28 (No.12). i233-i241. doi:10.1093/bioinformatics/bts222 ISSN 1367-4803.

[img]
Preview
Text
WRAP_Wild_Bioinformatics-2012-Penfold-i233-41.pdf - Published Version

Download (504Kb) | Preview
Official URL: http://dx.doi.org/10.1093/bioinformatics/bts222

Request Changes to record.

Abstract

Motivation: The generation of time series transcriptomic datasets collected under multiple experimental conditions has proven to be a powerful approach for disentangling complex biological processes, allowing for the reverse engineering of gene regulatory networks (GRNs). Most methods for reverse engineering GRNs from multiple datasets assume that each of the time series were generated from networks with identical topology. In this study, we outline a hierarchical, non-parametric Bayesian approach for reverse engineering GRNs using multiple time series that can be applied in a number of novel situations including: (i) where different, but overlapping sets of transcription factors are expected to bind in the different experimental conditions; that is, where switching events could potentially arise under the different treatments and (ii) for inference in evolutionary related species in which orthologous GRNs exist. More generally, the method can be used to identify context-specific regulation by leveraging time series gene expression data alongside methods that can identify putative lists of transcription factors or transcription factor targets.

Results: The hierarchical inference outperforms related (but non-hierarchical) approaches when the networks used to generate the data were identical, and performs comparably even when the networks used to generate data were independent. The method was subsequently used alongside yeast one hybrid and microarray time series data to infer potential transcriptional switches in Arabidopsis thaliana response to stress. The results confirm previous biological studies and allow for additional insights into gene regulation under various abiotic stresses.

Availability: The methods outlined in this article have been implemented in Matlab and are available on request.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Q Science > QH Natural history > QH301 Biology
Divisions: Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- )
Faculty of Science, Engineering and Medicine > Research Centres > Warwick Systems Biology Centre
Library of Congress Subject Headings (LCSH): Genetic regulation -- Statistical methods, Time-series analysis, Bayesian statistical decision theory
Journal or Publication Title: Bioinformatics
Publisher: Oxford University Press
ISSN: 1367-4803
Official Date: 2012
Dates:
DateEvent
2012Published
Volume: Vol.28
Number: No.12
Page Range: i233-i241
DOI: 10.1093/bioinformatics/bts222
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 22 December 2015
Date of first compliant Open Access: 22 December 2015
Funder: Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), Engineering and Physical Sciences Research Council (EPSRC)
Grant number: BB/F005806/1 (BBSRC), EP/I036575/1 (EPSRC)

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us