Type
Text
Type
Dissertation
Advisor
Schatz, Michael C | Skiena, Steven | Patro, Robert | Siepel, Adam | Heckerman, David.
Date
2015-12-01
Keywords
Computer science | Genome assembly, Long read sequencing technology, Machine Learning, Modeling and Prediction, PacBio, Support Vector Regression
Department
Department of Computer Science.
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/77294
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
The first generation sequencing technology represented by Sanger sequencing opened the era of de novo genome assembly. The assembled genome had good quality as a reference but it was very costly. The second generation, so called Next-Gen sequencing, produced tons of short reads in a day with economic cost. However de novo genome projects and other downstream research confronted limited quality. Now third generation begins with long reads sequencing technology and related algorithms with reasonable cost. The quality of reference genomes and downstream research has been recovered. Here we introduce third generation read sequencing technology and span sequencing technology, related algorithms and useful applications. Long read sequencing technology is expected to contribute significantly to the biology community by addressing the limitations from short read mapping and discovering novel biological importance. | 152 pages
Recommended Citation
Lee, Hayan, "Algorithms and Applications in Genome Assembly using Long Read Sequencing Technology" (2015). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 3115.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/3115