Precise estimation of mutation spectrum from deep sequencing genomic data
Abstract/Contents
- Abstract
- The mutation spectrum (MS) is the description of novel mutations. It represents the potential diversity of genetic possibilities available to an evolving population, and is the basis upon which selection acts. A precise understanding of the MS of any species is imperative for accurate modeling of the rate and dynamics of population evolution. However, spontaneous mutations are rare, the characterization of MS has been tedious and difficult, and a precise description of a genome-wide MS from a large number of mutational events wasn't available. In my thesis work, I take advantage of the advent of high-throughput sequencing, and approach the question in three ways. I show that pooled sequencing accurately reflects the SFS of a population with error well approximated by binomial sampling, and is a reliable means of sampling many populations cheaply. I directly survey the yeast MS through large-scale whole genome sequencing of 145 mutation accumulation lines and identify nearly 1000 spontaneous mutation events, the largest at the time. With whole genome sequences from a further 141 strains of yeast, I show that we can identify young polymorphic sites that show no signals of selection and display a mutation spectrum similar to that found in MA experiments. This technique can potentially be modified and applied to any deep population sequencing data, allowing the study of the MS of organisms not previously available through experimental means.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2014 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Zhu, Yuan |
|---|---|
| Associated with | Stanford University, Department of Genetics. |
| Primary advisor | Petrov, Dmitri Alex, 1969- |
| Thesis advisor | Petrov, Dmitri Alex, 1969- |
| Thesis advisor | Bustamante, Carlos |
| Thesis advisor | Sherlock, Gavin |
| Thesis advisor | Tang, Hua |
| Advisor | Bustamante, Carlos |
| Advisor | Sherlock, Gavin |
| Advisor | Tang, Hua |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Yuan Zhu. |
|---|---|
| Note | Submitted to the Department of Genetics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2014. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2014 by Yuan Zhu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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