کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6491827 | 43437 | 2013 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel approach for copy number variation analysis by combining multiplex PCR with matrix-assisted laser desorption ionization time-of-flight mass spectrometry
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
بیو مهندسی (مهندسی زیستی)
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
With the increasing interest in copy number variation as it pertains to human genomic variation, common phenotypes, and disease susceptibility, there is a pressing need for methods to accurately identify copy number. In this study, we developed a simple approach that combines multiplex PCR with matrix-assisted laser desorption ionization time-of-flight mass spectrometry for submicroscopic copy number variation detection. Two pairs of primers were used to simultaneously amplify query and endogenous control regions in the same reaction. Using a base extension reaction, the two amplicons were then distinguished and quantified in a mass spectrometry map. The peak ratio between the test region and the endogenous control region was manually calculated. The relative copy number could be determined by comparing the peak ratio between the test and control samples. This method generated a copy number measurement comparable to those produced by two other commonly used methods - multiplex ligation-dependent probe amplification and quantitative real-time PCR. Furthermore, it can discriminate a wide range of copy numbers. With a typical 384-format SpectroCHIP, at least six loci on 384 samples can be analyzed simultaneously in a hexaplex assay, making this assay adaptable for high throughput, and potentially applicable for large-scale association studies.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Biotechnology - Volume 166, Issues 1â2, 20 June 2013, Pages 6-11
Journal: Journal of Biotechnology - Volume 166, Issues 1â2, 20 June 2013, Pages 6-11
نویسندگان
Yonghui Gao, Xiaoli Chen, Jianhua Wang, Shaofang Shangguan, Yaohua Dai, Ting Zhang, Junling Liu,