- Title Information
- Title
- Improving Information Propagation in Phylogenetic Workflows
- Name:
Personal
- Name Part
- Guang, August
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Lawrence, Charles
- Role
- Role Term:
Text
- Advisor
- Name:
Personal
- Name Part
- Dunn, Casey
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Lewis, Paul
- Role
- Role Term:
Text
- Reader
- Name:
Corporate
- Name Part
- Brown University. Department of Applied Mathematics
- Role
- Role Term:
Text
- sponsor
- Origin Information
- Copyright Date
- 2018
- Physical Description
- Extent
- xiv, 100 p.
- digitalOrigin
- born digital
- Note:
thesis
- Thesis (Ph. D.)--Brown University, 2018
- Genre (aat)
- theses
- Abstract
- Despite the enormous amount of biological variation and technical uncertainty
in sequence data, most phylogenetic workflows propagate a single point estimate
throughout the numerous analysis components, and only in a forward direction. This
approach relies on three implicit assumptions: (i) the order of the analysis steps is biologically
justified, (ii) a Markovian dependency structure exists between analysis
components, and (iii) there is low relative entropy between results at each analysis
step. There is evidence that these assumptions, in particular low relative entropy, are
frequently violated in empirical studies with potential detrimental effects in phylogenetic
analyses.
In this thesis, I lay out a probabilistic framework that provides a unified perspective
to provide context for evaluating priorities for future developments of methods and
tools. I then develop a generative model of the natural and technical processes that
produce observed genomic reads within the framework that can be used to assess and
validate approaches that relax the implicit assumptions. Finally, I explore two ways to
accommodate and propagate more information in a phylogenetic workflow. The first
way, an HMM profile-sampling approach to genome assembly, relaxes the assumption
of low relative entropy in results from the genome assembly analysis component.
This approach finds relevant applications to HIV transmission networks. The second
way, an iterative approach to identifying and resolving transcriptome assembly errors,
capitalizes on the assumption of Markovian dependence.
- Subject
- Topic
- HIV/AIDS
- Subject
- Topic
- Probability
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00871990")
- Topic
- Computational biology
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01062326")
- Topic
- Phylogeny
- Language
- Language Term (ISO639-2B)
- English
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20180615
- Identifier:
DOI
- 10.26300/m4j5-dd88
- Access Condition:
rights statement
(href="http://rightsstatements.org/vocab/InC/1.0/")
- In Copyright
- Access Condition:
restriction on access
- Collection is open for research.
- Type of Resource (primo)
- dissertations