Gilad Yoav, Borevitz Justin
Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
Curr Opin Genet Dev. 2006 Dec;16(6):553-8. doi: 10.1016/j.gde.2006.09.005. Epub 2006 Sep 27.
The emerging field of genomics examines the relationship between genetic and phenotypic variation by describing and analyzing patterns of natural variation on a genome-wide scale. In this endeavor, an important tool is the use of microarrays, which enable simultaneous screening of thousands of assays. Microarrays were originally designed for the detection of differences between samples and are thus ideally suited to high-throughput studies of natural variation. Novel microarray platforms enable the high throughput survey of variation at multiple levels, including DNA sequences, gene expression, protein binding, and methylation. However, most microarray data analysis tools, notably normalization methods, were developed for experiments in which only few features differed between samples. In studies of natural variation, this assumption does not always hold, raising a number of new challenges.
新兴的基因组学领域通过在全基因组范围内描述和分析自然变异模式,来研究遗传变异与表型变异之间的关系。在这一过程中,一个重要的工具是使用微阵列,它能够同时筛选数千个检测项目。微阵列最初是为检测样本之间的差异而设计的,因此非常适合用于自然变异的高通量研究。新型微阵列平台能够在多个层面进行变异的高通量检测,包括DNA序列、基因表达、蛋白质结合和甲基化。然而,大多数微阵列数据分析工具,尤其是标准化方法,是为样本间只有少数特征不同的实验而开发的。在自然变异研究中,这一假设并不总是成立,从而带来了许多新的挑战。