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首页> 外文期刊>Nucleic acids research >Microarray labeling extension values: laboratory signatures for Affymetrix GeneChips
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Microarray labeling extension values: laboratory signatures for Affymetrix GeneChips

机译:微阵列标签扩展值:Affymetrix GeneChips的实验室签名

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摘要

Interlaboratory comparison of microarray data, even when using the same platform, imposes several challenges to scientists. RNA quality, RNA labeling efficiency, hybridization procedures and data-mining tools can all contribute variations in each laboratory. In Affymetrix GeneChips, about 11–20 different 25-mer oligonucleotides are used to measure the level of each transcript. Here, we report that ‘labeling extension values (LEVs)', which are correlation coefficients between probe intensities and probe positions, are highly correlated with the gene expression levels (GEVs) on eukayotic Affymetrix microarray data. By analyzing LEVs and GEVs in the publicly available 2414 cel files of 20 Affymetrix microarray types covering 13 species, we found that correlations between LEVs and GEVs only exist in eukaryotic RNAs, but not in prokaryotic ones. Surprisingly, Affymetrix results of the same specimens that were analyzed in different laboratories could be clearly differentiated only by LEVs, leading to the identification of ‘laboratory signatures'. In the examined dataset, GSE10797, filtering out high-LEV genes did not compromise the discovery of biological processes that are constructed by differentially expressed genes. In conclusion, LEVs provide a new filtering parameter for microarray analysis of gene expression and it may improve the inter- and intralaboratory comparability of Affymetrix GeneChips data.
机译:即使在使用相同平台的情况下,微阵列数据的实验室间比较也给科学家带来了一些挑战。 RNA的质量,RNA的标记效率,杂交程序和数据挖掘工具都可以为每个实验室带来变化。在Affymetrix基因芯片中,大约11-20个不同的25-mer寡核苷酸用于测量每个转录本的水平。在这里,我们报道了“标记延伸值(LEV)”,它是探针强度与探针位置之间的相关系数,与真核Affymetrix微阵列数据上的基因表达水平(GEV)高度相关。通过分析20种Affymetrix微阵列类型的公开可用的2414 cel文件中的LEV和GEV,这些文件涵盖13个物种,我们发现LEV和GEV之间的相关性仅存在于真核RNA中,而在原核RNA中不存在。出人意料的是,仅在实验室中,可以通过LEV来区分不同实验室分析的同一样本的Affymetrix结果,从而鉴定出“实验室特征”。在检查的数据集GSE10797中,滤除高LEV基因不会损害由差异表达基因构建的生物过程的发现。总之,LEV为基因表达的微阵列分析提供了新的过滤参数,它可以改善Affymetrix GeneChips数据的实验室间和实验室内的可比性。

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