DOI

https://doi.org/10.25772/BP5V-EP74

Author ORCID Identifier

orcid.org/0000-0002-4709-0096

Defense Date

2016

Document Type

Thesis

Degree Name

Master of Science

Department

Bioinformatics

First Advisor

Dr. Paul Brooks

Second Advisor

Dr. Stephen Fong

Third Advisor

Dr. Maria Rivera

Abstract

The decreasing costs of next generation sequencing technologies and the increasing speeds at which they work have lead to an abundance of 'omic datasets. The need for tools and methods to analyze, annotate, and model these datasets to better understand biological systems is growing. Here we present a novel software pipeline to reconstruct the metabolic model of an organism in silico starting from its genome sequence and a novel compilation of biological databases to better serve the generation of metabolic models. We validate these methods using five Gardnerella vaginalis strains and compare the gene annotation results to NCBI and the FBA results to Model SEED models. We found that our gene annotations were larger and highly similar in terms of function and gene types to the gene annotations downloaded from NCBI. Further, we found that our FBA models required a minimal addition of transport reactions, sources, and escapes indicating that our draft pathway models were very complete. We also found that on average our solutions contained more reactions than the models obtained from Model SEED due to a large amount of baseline reactions and gene products found in ASGARD.

Rights

©2016 Shaun William Norris

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

12-13-2016

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