Return on investment and library complexity analysis for DNA sequencing
Name
958632049-MIT.pdf
Description
Full printable version
Size
6.96 MB
Format
Adobe PDF
Checksum (MD5)
9dbdd4e3097a8bb8013167a33485aa87
Author(s)
Hogstrom, Larson J
Advisor(s)
Yossi Farjoun and Bonnie Berger.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
Understanding the profiles of information acquisition during DNA sequencing experiments is critical to the design and implementation of large-scale studies in medical and population genetics. One known technical challenge and cost driver in next-generation sequencing data is the occurrence of non-independent observations that are created from sequencing artifacts and duplication events from polymerase chain reaction (PCR). The current study demonstrates improved return on investment (ROI) modeling strategies to better anticipate the impact of non-independent observations in multiple forms of next-generation sequencing data. Here, a physical modeling approach based on Pó1ya urn was evaluated using both multi-point estimation and duplicate set occupancy vectors. The results of this study can be used to reduce sequencing costs by improving aspects of experimental design including sample pooling strategies, top-up events, and termination of non-informative samples.
Description
Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 49).
Subjects
Computation for Design and Optimization Program.
MIT Department
Massachusetts Institute of Technology. Computation for Design and Optimization Program
Terms of Use
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
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