A Computational Approach to Detect CNVs Using High-throughput Sequencing

被引：1

作者：

Moon, Myungjin ^{[1
]}

Ahn, Jaegyoon ^{[1
]}

Park, Chihyun ^{[1
]}

Park, Sanghyun ^{[1
]}

Yoon, Youngmi ^{[2
]}

Yoon, Jeehee ^{[3
]}

机构：

[1] Yonsei Univ, Dept Comp Sci, Seoul 120749, South Korea

[2] Gachon Univ Med & Sci, Dept Informat Technol, Seoul 120749, South Korea

[3] Hallym Univ, Div Informat & Commun Engn, Seoul 120749, South Korea

来源：

2009 9TH IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING | 2009年

关键词：

Copy Number Variations; CNVs; High-throughput sequencing; Genomic variants generator; COPY-NUMBER VARIATION; SHORT DNA-SEQUENCES; HUMAN GENOME; STRUCTURAL VARIATION; ACCURATE;

D O I：

10.1109/BIBE.2009.13

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Copy-Number Variations (CNVs) can be defined as gains or losses that are greater than 1kbs of genomic DNA among phenotypically normal individuals. CNVs detected by microarray based approach are limited to medium or large sized ones because of its low resolution. Here we propose a novel approach to detect CNVs by aligning the short reads obtained by high-throughput sequencer to the previously assembled human genome sequence, and analyzing the distribution of the aligned reads. Application of our algorithm demonstrates the feasibility of detecting CNVs of arbitrary length, which include short ones that microarray based algorithms cannot detect. Also, false positive and false negative rates of the results were relatively low compared to those of microarray based algorithms.

引用

页码：266 / +

页数：2

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