Lu Jun, Lee Joseph C, Salit Marc L, Cam Margaret C
Genomics Core Laboratory, National Institute of Diabetes & Digestive & Kidney Diseases, National Institutes of Health, Bethesda, MD 20892 USA.
BMC Bioinformatics. 2007 Mar 29;8:108. doi: 10.1186/1471-2105-8-108.
Extracting biological information from high-density Affymetrix arrays is a multi-step process that begins with the accurate annotation of microarray probes. Shortfalls in the original Affymetrix probe annotation have been described; however, few studies have provided rigorous solutions for routine data analysis.
Using AceView, a comprehensive human transcript database, we have reannotated the probes by matching them to RNA transcripts instead of genes. Based on this transcript-level annotation, a new probe set definition was created in which every probe in a probe set maps to a common set of AceView gene transcripts. In addition, using artificial data sets we identified that a minimal probe set size of 4 is necessary for reliable statistical summarization. We further demonstrate that applying the new probe set definition can detect specific transcript variants contributing to differential expression and it also improves cross-platform concordance.
We conclude that our transcript-level reannotation and redefinition of probe sets complement the original Affymetrix design. Redefinitions introduce probe sets whose sizes may not support reliable statistical summarization; therefore, we advocate using our transcript-level mapping redefinition in a secondary analysis step rather than as a replacement. Knowing which specific transcripts are differentially expressed is important to properly design probe/primer pairs for validation purposes. For convenience, we have created custom chip-description-files (CDFs) and annotation files for our new probe set definitions that are compatible with Bioconductor, Affymetrix Expression Console or third party software.
从高密度Affymetrix芯片中提取生物学信息是一个多步骤过程，始于对微阵列探针的准确注释。已有人描述过原始Affymetrix探针注释存在的不足；然而，很少有研究为常规数据分析提供严格的解决方案。
我们使用一个全面的人类转录本数据库AceView，通过将探针与RNA转录本而非基因进行匹配，对探针进行了重新注释。基于这种转录本水平的注释，创建了一个新的探针集定义，其中一个探针集中的每个探针都映射到一组共同的AceView基因转录本。此外，使用人工数据集我们确定，可靠的统计汇总所需的最小探针集大小为4。我们进一步证明，应用新的探针集定义可以检测出导致差异表达的特定转录本变体，并且还提高了跨平台一致性。
我们得出结论，我们对转录本水平的重新注释和探针集的重新定义补充了原始的Affymetrix设计。重新定义引入的探针集大小可能不支持可靠的统计汇总；因此，我们主张在二次分析步骤中使用我们的转录本水平映射重新定义，而不是作为替代。了解哪些特定转录本差异表达对于正确设计用于验证目的的探针/引物对很重要。为方便起见，我们为新的探针集定义创建了与Bioconductor、Affymetrix Expression Console或第三方软件兼容的自定义芯片描述文件（CDF）和注释文件。
