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Abstract Details
Activity Number:
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33
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #305838 |
Title:
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On the Analysis of Next-Generation Sequencing Data for Identifying CNV Regions
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Author(s):
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Jie Chen*+ and Ayten Yigiter
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Companies:
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University of Missouri-Kansas City and Hacettepe University
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Address:
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Mathematics and Statistics 206 Haag Hall, Kansas City, MO, 64110-2481, United States
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Keywords:
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change point analysis ;
DNA copy number ;
hypothesis testing ;
estimation ;
next-generation sequencing data
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Abstract:
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In this paper, we propose to use a change point analysis approach for detecting DNA copy number variations (CNVs) in the massive and high throughput next generation sequencing, or high throughput sequencing (HTS) data of reads. It is known that CNVs are common in cancer, genetic disorders and other diseases. The bridge from sequencing data to medical diagnostics of diseases such as cancer, osteoporosis, and genetic disorders, is built on a solid statistical analysis of the information rich HTS data. We will use a modified information criterion to identify the boundaries of CNV regions on the genome under a new framework. Estimation of the true copy numbers in each CNV region will be given. Applications to simulated sequencing data and publically available next generation sequencing data will be given.
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