Pariset Lorraine, Chillemi Giovanni, Bongiorni Silvia, Romano Spica Vincenzo, Valentini Alessio
Department of Animal Production, Università della Tuscia, Viterbo, Italy.
N Biotechnol. 2009 Jun;25(5):272-9. doi: 10.1016/j.nbt.2009.03.013. Epub 2009 Apr 5.
Microarrays produce a measurement of gene expression based on the relative measures of dye intensities that correspond to the amount of target RNA. This technology is fast developing and its application is expanding from Homo sapiens to a wide number of species, where enough information on sequences and annotations exist. Anyway, the number of species for which a dedicated platform exists is not high. The use of heterologous array hybridization, screening for gene expression in one species using an array developed for another one, is still quite frequent, even though cross-species microarray hybridization has raised many arguments. Some methods which are high throughput and do not rely on knowledge of the DNA/RNA sequence exist, namely serial analysis of gene expression (SAGE), Massively Parallel Signature Sequencing (MPSS) and deep sequencing of full transcriptome. Although very powerful, particularly the latter, they are still quite costly and cumbersome methods. In some species where genome sequences are largely unknown, several anonymous sequences are deposited in gene banks as a result of Expressed Sequence Tags (ESTs) sequencing projects. The ESTs databases represent a valuable knowledge that can be exploited with some bioinformatic effort to build species-specific microarrays. We present here a method of high-density in situ synthesized microarrays starting from available EST sequences in, Ovis aries. Our data indicate that the method is very efficient and can be easily extended to other species of which genetic sequences are present in public databases, but neglected so far with advanced devices like microarrays. As a perspective, the approach can be applied also to species of which no sequences are available to date, thanks to high-throughput deep sequencing methods.
微阵列基于与靶RNA量相对应的染料强度相对测量值来产生基因表达的测量结果。这项技术发展迅速，其应用正从智人扩展到大量有足够序列和注释信息的物种。无论如何，拥有专用平台的物种数量并不多。使用异源阵列杂交，即使用为另一个物种开发的阵列来筛选一个物种中的基因表达，仍然相当频繁，尽管跨物种微阵列杂交引发了许多争议。存在一些高通量且不依赖DNA/RNA序列知识的方法，即基因表达序列分析（SAGE）、大规模平行签名测序（MPSS）和全转录组深度测序。尽管非常强大，尤其是后者，但它们仍然是相当昂贵且繁琐的方法。在一些基因组序列大多未知的物种中，由于表达序列标签（EST）测序项目，一些匿名序列被存入基因库。EST数据库代表了一种有价值的知识，可以通过一些生物信息学努力加以利用，以构建物种特异性微阵列。我们在此展示一种从绵羊现有的EST序列出发进行高密度原位合成微阵列的方法。我们的数据表明该方法非常有效，并且可以很容易地扩展到其他在公共数据库中有遗传序列但迄今被像微阵列这样的先进设备忽视的物种。从长远来看，由于高通量深度测序方法，该方法也可以应用于迄今没有可用序列的物种。
