BBSRC Portfolio Analyser
Award details
Markov Chain Monte Carlo comparative statistical methods to account for phylogetic uncertainty
Reference
G14980
Principal Investigator / Supervisor
Professor Mark Pagel
Co-Investigators /
Co-Supervisors
Institution
University of Reading
Department
Animal and Microbial Sciences
Funding type
Research
Value (£)
153,032
Status
Completed
Type
Research Grant
Start date
01/10/2001
End date
31/10/2005
Duration
49 months
Abstract
The aim of this research is to develop Markov Chain Monte Carlo (MCMC) comparative methods for the across-species analysis of gene-sequence and other discrete trait evolution on phylogenetic trees. Phylogenies are treated as known without error in comparative studies, even though trees are rarely known with certainty. MCMC methods provide a formal approach to solve the problem of phylogenetic uncertainty. This research will investigate three approaches to combining comparative statistical methods into the MCMC theoretical framework. We shall conduct computer simulation studies of the methods, apply them to real gene-sequence data sets, and develop user-friendly software to be made freely available to researchers.
Summary
unavailable
Committee
Closed Committee - Genes & Developmental Biology (GDB)
Research Topics
X – not assigned to a current Research Topic
Research Priority
X – Research Priority information not available
Research Initiative
X - not in an Initiative
Funding Scheme
X – not Funded via a specific Funding Scheme
I accept the
terms and conditions of use
(opens in new window)
export PDF file
back to list
new search