Detection and validation of genomic structural variation from DNA sequencing data
- Joseph G. Arthur.
- [Stanford, California] : [Stanford University], 2018.
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|3781 2018 A||In-library use|
- The comparison of individual genome sequences is a key task for modern studies of population genetics, genotype-phenotype associations, and genome evolution. The problem is difficult in part because commonly-used DNA sequencing hardware produces reads that are orders of magnitude smaller than the size of a single human chromosome. The detection of large genomic mutations known as structural variants (SVs) from these short sequencing reads has emerged has a particularly challenging problem. Numerous methods targeting this problem have been proposed, but it is difficult to assess their performance on real data since the ground truth is typically unknown. Moreover, complex SVs that escape detection by conventional algorithms are known to exist. We propose here a solution to both the complex SV detection problem and the issue of evaluating accuracy on real data.
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- Submitted to the Department of Statistics.
- Thesis Ph.D. Stanford University 2018.
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