Food Nutrition Genomics, Agri-Foods and Health Section, AgResearch Grasslands, Tennent Drive, New Zealand.
Brief Funct Genomics. 2011 May;10(3):135-50. doi: 10.1093/bfgp/elr005. Epub 2011 Mar 9.
This review focuses on tools for studying a cell's transcriptome, the collection of all RNA transcripts produced at a specific time, and the tools available for determining how these changes in gene expression relate to the functional changes in an organism. While the microarray-based (analog) gene-expression profiling technology has dominated the 'omics' era, Next-Generation Sequencing based gene-expression profiling (RNA-Seq) is likely to replace this analog technology in the future. RNA-Seq shows much promise for transcriptomic studies as the genes of interest do not have to be known a priori, new classes of RNA, SNPs and alternative splice variants can be detected, and it is also theoretically possible to detect transcripts from all biologically relevant abundance classes. However, the technology also brings with it new issues to resolve: the specific technical properties of RNA-Seq data differ to those of analog data, leading to novel systematic biases which must be accounted for when analysing this type of data. Additionally, multireads and splice junctions can cause problems when mapping the sequences back to a genome, and concepts such as cloud computing may be required because of the massive amounts of data generated.
这篇综述重点介绍了研究细胞转录组的工具，即特定时间产生的所有 RNA 转录本的集合，以及用于确定这些基因表达变化与生物体功能变化之间关系的工具。虽然基于微阵列的（模拟）基因表达谱分析技术在“组学”时代占据主导地位，但基于下一代测序的基因表达谱分析（RNA-Seq）可能会在未来取代这种模拟技术。RNA-Seq 为转录组学研究带来了很大的希望，因为不需要事先知道感兴趣的基因，新的 RNA 类别、SNP 和可变剪接变体都可以被检测到，而且从所有具有生物学相关性的丰度类别中检测转录本在理论上也是可能的。然而，该技术也带来了需要解决的新问题：RNA-Seq 数据的特定技术特性与模拟数据不同，导致在分析这种类型的数据时必须考虑到新的系统偏差。此外，多读取和剪接接头在将序列映射回基因组时可能会引起问题，并且由于生成的大量数据，可能需要云计算等概念。
