Theses and Dissertations

Nan Wang

Issuing Body

Mississippi State University

Advisor

Bridges, Susan

Committee Member

Hansen, Eric

Committee Member

Luke, Edward

Committee Member

Yuan, Changhe

Committee Member

Burgess, Shane

Date of Degree

8-8-2009

Document Type

Dissertation - Open Access

Major

Computer Science

Degree Name

Doctor of Philosophy (Ph.D)

College

James Worth Bagley College of Engineering

Department

Department of Computer Science and Engineering

Abstract

In this dissertation, we describe a new approach for gene finding that can utilize proteomics information in addition to DNA and RNA to identify new genes in prokaryote genomes. Proteomics processing pipelines require identification of small pieces of proteins called peptides. Peptide identification is a very error-prone process and we have developed a new algorithm for validating peptide identifications using a distance-based outlier detection method. We demonstrate that our method identifies more peptides than other popular methods using standard mixtures of known proteins. In addition, our algorithm provides a much more accurate estimate of the false discovery rate than other methods. Once peptides have been identified and validated, we use a second algorithm, proteogenomic mapping (PGM) to map these peptides to the genome to find the genetic signals that allow us to identify potential novel protein coding genes called expressed Protein Sequence Tags (ePSTs). We then collect and combine evidence for ePSTs we generated, and evaluate the likelihood that each ePST represents a true new protein coding gene using supervised machine learning techniques. We use machine learning approaches to evaluate the likelihood that the ePSTs represent new genes. Finally, we have developed new approaches to Bayesian learning that allow us to model the knowledge domain from sparse biological datasets. We have developed two new bootstrap approaches that utilize resampling to build networks with the most robust features that reoccur in many networks. These bootstrap methods yield improved prediction accuracy. We have also developed an unsupervised Bayesian network structure learning method that can be used when training data is not available or when labels may not be reliable.

URI

https://hdl.handle.net/11668/19379

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