Probe selection for high-density oligonucleotide arrays
Probe selection for high-density oligonucleotide arrays
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DOI：
10.1073/pnas.1534744100
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发表时间：
2003-09-30
影响因子：
11.1
通讯作者：
Webster, TA
中科院分区：
文献类型：
--
作者：
Mei, R;Hubbell, E;Webster, TA
高密度寡核苷酸微阵列能够同时监测数万种转录物的表达水平。为了在细胞mRNA存在下准确检测和定量转录物，设计其寡核苷酸探针产生准确反映原始mRNA浓度的杂交强度的微阵列是必不可少的。我们提出了一个基于模型的方法，预测最佳的探针，通过使用序列和经验信息。我们建立了杂化行为的热力学模型，并确定了经验因素对有效拟合参数的影响。我们设计了包含数百种人类和酵母转录物的所有25聚体探针的Affyssin基因芯片探针阵列，并收集了超过4,000倍浓度范围的数据。建立多元线性回归模型预测杂交强度！的每个探针在给定的目标浓度，和每个城市间的概况总结了一个探针响应度量。我们选择了探针组来代表每个转录本，这些转录本在响应性、独立性（探针序列不重叠的程度）和独特性（与表达的基因组背景中的序列缺乏相似性）方面进行了优化。我们对这种方法如何能够为高密度寡核苷酸阵列选择具有高灵敏度和特异性的探针进行了研究，
High-density oligonucleotide microarrays enable simultaneous mon toring of expression levels of tens of thousands of transcripts. For accurate detection and quantitation of transcripts in the presence of cellular mRNA, it is essential to design microarrays whose oligonucleotide probes produce hybridization intensities that accurately reflect the concentration of original mRNA. We present a model-based approach that predicts optimal probes by using sequence and empirical information. We constructed a thermodynamic model for hybridization behavior and determined the influence of empirical factors on the effective fitting parameters. We designed Affymetrix GeneChip probe arrays that contained all 25-mer probes for hundreds of human and yeast transcripts and collected data over a 4,000-fold concentration range. Multiple linear regression models were built to predict hybridization intensities! of each probe at given target concentrations, and each inter city profile is summarized by a probe response metric. We selected probe sets to represent each transcript that were optimize I with respect to responsiveness, independence (degree to which probe sequences are nonoverlapping), and uniqueness (lack of similarity to sequences in the expressed genomic background). We 5 how that this approach is capable of selecting probes with high sensitivity and specificity for high-density oligonucleotide array,