Gene prediction with conditional random fields

The accurate annotation of an organism's protein-coding genes is crucial for subsequent genomic analysis. The rapid advance of sequencing technology has created a gap between genomic sequences and their annotations. Automated annotation methods are needed to bridge this gap, but existing solutions based on hidden Markov models cannot easily incorporate diverse evidence to make more accurate predictions. In this thesis, I built upon the semi-Markov conditional random field framework created by DeCaprio et al. to predict protein-coding genes in DNA sequences. Several novel extensions were designed and implemented, including a 29-state model with both semi-Markov and Markov states, an N-best Viterbi inference algorithm, several classes of discriminative feature functions that incorporate diverse evidence, and parallelization of the training and inference algorithms. The extensions were tested on the genomes of Phytophthora infestans, Culex pipiens, and Homo sapiens. The gene predictions were analyzed and the benefits of discriminative methods were explored.

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

2007

Author dc:creator

• Doherty, Matthew K

Advisor dc:contributor.advisor

• James Galagan and David DeCaprio.

• 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/1721.1/41646

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/41646