In-depth cDNA Library Sequencing Provides Quantitative Gene Expression Profiling in Cancer Biomarker Discovery
In-depth cDNA Library Sequencing Provides Quantitative Gene Expression Profiling in Cancer Biomarker Discovery
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DOI：
10.1016/s1672-0229(08)60028-5
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发表时间：
2009-06-01
影响因子：
9.5
通讯作者：
Lau, Yu-Lung
中科院分区：
文献类型：
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作者：
Yang, Wanling;Ying, Dingge;Lau, Yu-Lung
基因表达定量分析在鉴定不同病理状态下差异表达基因、基因表达调控和共调控、揭示基因功能等方面发挥着重要作用。尽管微阵列被广泛用作这方面的有力工具，但它在定量上是次优的，并且无法检测未知的基因变异。在这里，我们证明了有效的检测差异表达和某些基因的共调控表达序列标签分析使用选定的cDNA文库的子集。我们讨论了测序深度和文库制备的问题，并提出增加测序深度和改进的制备程序可以检测丰度较低的基因变体的许多表达特征。随着测序成本的降低和新一代测序技术的出现，cDNA池或文库的深度测序可能代表基因表达谱和癌症生物标志物检测的更好和更强大的工具。我们还建议使用序列特异性减法去除数百个最丰富的管家基因，以增加测序深度，而不影响其他基因的相对表达率，因为来自少至300个最丰富表达的基因的转录本占总转录组的约20%。深度测序还代表了检测未知形式的转录物的独特优势，例如选择性剪接变体，融合基因和调控RNA，以及检测可能在疾病发病机制中起重要作用的突变和多态性。
Quantitative gene expression analysis plays an important role in identifying differentially expressed genes in various pathological states, gene expression regulation and co-regulation, shedding light on gene functions. Although microarray is widely used as a powerful tool in this regard, it is suboptimal quantitatively and unable to detect unknown gene variants. Here we demonstrated effective detection of differential expression and co-regulation of certain genes by expressed sequence tag analysis using a selected subset of cDNA libraries. We discussed the issues of sequencing depth and library preparation, and propose that increased sequencing depth and improved preparation procedures may allow detection of many expression features for less abundant gene variants. With the reduction of sequencing cost and the emerging of new generation sequencing technology, in-depth sequencing of cDNA pools or libraries may represent a better and powerful tool in gene expression profiling and cancer biomarker detection. We also propose using sequence-specific subtraction to remove hundreds of the most abundant housekeeping genes to increase sequencing depth without affecting relative expression ratio of other genes, as transcripts from as few as 300 most abundantly expressed genes constitute about 20% of the total transcriptome. In-depth sequencing also represents a unique advantage of detecting unknown forms of transcripts, such as alternative splicing variants, fusion genes, and regulatory RNAs, as well as detecting mutations and polymorphisms that may play important roles in disease pathogenesis.