DNA Copy Number Data Analysis in Human Genomes
Date Issued
2008
Date
2008
Author(s)
Chen, Peng-An
Abstract
Copy number variations (CNVs) are one kind of structural variations in the human genome and are associated with many genetic diseases. Array CGH approaches can provide biologists high resolution analysis of DNA copy number data. Since the resolution of array CGH approaches is increasing, the signal-to-noise ratio is also getting higher in recent array CGH approaches. To handle the noise in the array CGH approaches, the experimental results are further analyzed to locate the copy number variations in the human genome. Lipson et al. [Journal of Computational Biology, 13(2):215-228, 2006] propose a statistical framework which enables us to find the boundaries of copy number variations in the human genome accurately and provides the significance for each aberration calling. It is assumed that the noise in the array CGH data is normally distributed in the framework. However, there is no evidence supporting this assumption. Furthermore, many statistical approaches also suffer this problem. In this thesis, we propose an improved framework without making the assumption. We also develop a systematic method for selecting the parameters in our framework. A linear time algorithm proposed by Bernholt et al. [7th Latin Americal Symposium, pages 178-189, 2006] is used to find copy number variations under this framework. However, their algorithm cannot find duplication events and deletion events of the human genome separately. Thus, a linear time algorithm for this purpose is proposed. We demonstrate the power of our methods by applying them to an array CGH dataset from leukemia patients. Our methods locate the CNVs in the array CGH data more accurately and finds regions which contain genes related to the acute myeloid leukemia.
Subjects
Copy Number Variation
Array Comparative Genomic Hybridization
Acute Myeloid Leukemia
SDGs
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-97-R95922022-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):b7f473384ef23a127ee7e50b4dafc944