کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1174487 961753 2008 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A dual-probe hybridization method for reducing variability in single nucleotide polymorphism analysis with oligonucleotide microarrays
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
A dual-probe hybridization method for reducing variability in single nucleotide polymorphism analysis with oligonucleotide microarrays
چکیده انگلیسی
DNA microarray technology has become powerful and popular in mutation/single nucleotide polymorphism (SNP) discovery and genotyping. However, this method is often associated with considerable signal noise of nonbiological origin that may compromise the data quality and interpretation. To achieve a high degree of reliability, accuracy, and sensitivity in data analysis, an effective normalization method to minimize the technical variability is highly desired. In the current study, a simple and robust normalization method is described. The method is based on introduction of a reference probe coimmobilized with SNP probes on the microarray for a dual-probe hybridization (DPH) reaction. The reference probe is used as an intraspot control for the customized microarrays. Using this method, the interassay coefficient of variation (CV) was reduced significantly by approximately 10%. After DPH normalization, the CVs and ranges of the ratios were reduced by two to five times. The relative magnitudes of variation of different sources were also analyzed by analysis of variance. Glass slides were shown to contribute the most to the variance, whereas sampling and residual errors had relatively modest contribution. The results showed that this DPH-based spot-dependent normalization method is an effective solution for reducing experimental variation associated with microarray genotyping data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Analytical Biochemistry - Volume 383, Issue 2, 15 December 2008, Pages 270-278
نویسندگان
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