Stochastic modeling of biological sequence evolution

Markov models of sequence evolution are a fundamental building block for making inferences in biological research. This thesis reviews several major techniques developed to estimate parameters of Markov models of sequence evolution and presents a new approach for evaluating and comparing estimation techniques. Current methods for evaluating estimation techniques require sequence data from populations with well-known phylogenetic relationships. Such data is not always available since phylogenetic relationships can never be known with certainty. We propose generating sequence data for the purpose of estimation technique evaluation by simulating sequence evolution in a controlled setting. Our elementary simulator uses a Markov model and a binary branching process, which dynamically builds a phylogenetic tree from an initial seed sequence. The sequences at the leaves of the tree can then be used as input to estimation techniques. We demonstrate our evaluation approach on Arvestad and Bruno's estimation method, and show how our approach can reveal performance variations empirically. The results of our simulation can be used as a guide towards improving estimation techniques.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2005

Author dc:creator

• Xu, Keyuan

Advisor dc:contributor.advisor

• George C. Verghese and Peter C. Doerschuk.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/32113

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/32113