Correct identification of genes from serial analysis of gene expression tag sequences

SAGE (serial analysis of gene expression) is a remarkable technique for genome-wide analysis of gene expression. It is crucial to understand the extent to which SAGE can accurately indicate a gene or expressed sequence tag (EST) with a single tag. We analyzed the effect of the size of SAGE tag on gene identification. Our observation indicates that SAGE tags are in general not long enough to achieve the degree of uniqueness of identification originally envisaged. Our observations also indicate that the limitation of using SAGE tag to identify a gene can be overcome by converting SAGE tags into longer 3' EST sequences with the generation of longer cDNA fragments from SAGE tages for gene identification (GLGI) method.     

开放的差异基因表达技术研究进展

自 90年代早期发展以来 ,差异基因表达 (DGE)技术在许多领域得到了应用 .“开放”结构系统的DGE技术不需原始的生物学或序列信息 ,而且可应用于任何种群 .主要介绍 6项开放的DGE技术 :cDNA代表性差示分析 (cDNA RDA)、基因表达系统分析 (SAGE)、表达序列标签串联排列连接(TALEST) ,和早期的DGE技术差异显示 (DD)、随机引物聚合酶链反应 (AP PCR) ,以及一项受专利保护的技术———GeneCalling .通过几项重要的参数对这些技术进行了比较 ,认为DD虽然有其致命的弱点 ,但在目前仍然应用得非常广泛 .cDNA RDA能有效富增特异片段 ,扣除共有序列 ,如能和SAGE结合 ,将能进一步促进其发展 .TALEST和GeneCalling操作较简便 ,一次试验能获得大量的数据 ,但是分析这些数据比较麻烦 ,须借助另外的分析软件 .最后介绍了应用DGE技术取得的最新成果 .     

Generation of high-quantity and quality tag/ditag cDNAs for SAGE analysis

The serial analysis of gene expression (SAGE) technique is an important tool for genome-wide gene expression analysis. However, the requirement of a large amount of mRNA for the analysis and the difficulties in generating high-quality tag and ditag fragments for the construction of a SAGE library often interfere with the successful performance of the SAGE technique. We developed two procedures to solve these issues: (i) introducing low-cycle PCR amplification of the 3' cDNA before the BsmFI digestion of the 3' cDNAs and (ii) gel purifying the BsmFI-released tag fragments before ditag formation. These modifications provide a large quantity of initial 3' cDNAs and high-quality tags and ditags for the construction of SAGE libraries.     

Extract‐SAGE: An integrated platform for cross­analysis and GA­based selection of SAGE data

Serial analysis of gene expression (SAGE) is a powerful quantification technique for gene expression data. The huge amount of tag data in SAGE libraries of samples is difficult to analyze with current SAGE analysis tools. Data is often not provided in a biologically significant way for cross‐analysis and ‐comparison, thus limiting its application. Hence, an integrated software platform that can perform such a complex task is required. Here, we implement set theory for cross‐analyzing gene expression data among different SAGE libraries of tissue sources; up‐ or down‐regulated tissue‐specific tags can be identified computationally. Extract‐SAGE employs a genetic algorithm (GA) to reduce the number of genes among the SAGE libraries. Its representative tag mining will facilitate the discovery of the candidate genes with discriminating gene expression.