Algorithms for the alignment of biological sequences
Abstract/Contents
- Abstract
- High throughput sequencing of biological sequences has become a core component of many experiments in biology. An important step in the analysis of these biological sequences is alignment to a reference genome. Alignment algorithms have existed for several decades and have evolved as sequencing technologies have progressed. The recent trend in sequencing is to generate longer reads at high throughput. This thesis introduces two new methods designed for this new type of data. The first method is for DNA sequences. By taking advantage of the longer reads, we develop a method that is several times faster than current approaches while improving the accuracy. We also propose a novel approach to reduce the memory requirement. This method is compared with popular approaches in both alignment accuracy and also the accuracy of the resulting variants called. The second method is for mRNA sequences. It leverages our approach to aligning DNA reads to align long mRNA reads. The unavoidable introns in mRNA sequences split a read into shorter sub-sequences, which makes alignment more difficult. We describe a method to align these intron junctions, resulting an method that is both faster and more accurate than current approaches. Our method is compared to a number of popular approaches based on both alignment accuracy and junction detection accuracy. Overall, the data generated by new sequencing platforms warrant development of new methods as significant improvement in speed and accuracy can be gained.
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 | Mu, John Chong |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Wong, Wing Hung |
| Thesis advisor | Wong, Wing Hung |
| Thesis advisor | Dill, David L |
| Thesis advisor | Montanari, Andrea |
| Thesis advisor | Weissman, Tsachy |
| Advisor | Dill, David L |
| Advisor | Montanari, Andrea |
| Advisor | Weissman, Tsachy |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | John Chong Mu. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2014. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2014 by John Chong Mu
- License
- This work is licensed under a Creative Commons Attribution No Derivatives 3.0 Unported license (CC BY-ND).
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