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.     

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.     

Enhanced concatemer cloning-a modification to the SAGE (Serial Analysis of Gene Expression) technique.

The Serial Analysis of Gene Expression (SAGE) method, described in 1995 by Velculescu et al ., represents a powerful means to compare gene expression between two mRNA populations. An improvement to SAGE that removes contaminating linker molecules, which compromise the efficiency of the method, has been developed. This modification utilises biotinylated PCR primers, which generate biotinylated linkers at an early stage in the SAGE protocol, thus allowing removal of the unwanted linkers by binding to streptavidin-coated magnetic beads at a later stage. The application of this modification resulted in the rapid generation of high ditag yields and clones with large average insert sizes.     

Consistent production of cost-effective longSAGE libraries

Serial analysis of gene expression (SAGE) and related techniques are gaining popularity as tools for exploring expression of plant genes but remain suboptimal because of smaller-than-expected average concatemer sizes. The presence of low-molecular-weight contaminants in high-molecular-weight concatemer fractions reduces the average size of cloned fragments, thereby limiting the viability of high-throughput sequencing methods. Implementation of an additional digestion step to promote formation of linear concatemer fragments appears to reduce the proportion of contaminants indirectly, but with variable results. We explored the effect of initial ditag polymerase chain reaction (PCR) quantity on the average size of cloned concatemers from the greater than 1000-bp fraction. The quantity of PCR material used was found to have a strong influence on the frequency of low-molecular-weight contaminants within this fraction, which has important implications for reducing costs associated with high-throughput sequencing of concatemer clones.     

A comparative analysis of the information content in long and short SAGE libraries

Background  

Serial Analysis of Gene Expression (SAGE) is a powerful tool to determine gene expression profiles. Two types of SAGE libraries, ShortSAGE and LongSAGE, are classified based on the length of the SAGE tag (10 vs. 17 basepairs). LongSAGE libraries are thought to be more useful than ShortSAGE libraries, but their information content has not been widely compared. To dissect the differences between these two types of libraries, we utilized four libraries (two LongSAGE and two ShortSAGE libraries) generated from the hippocampus of Alzheimer and control samples. In addition, we generated two additional short SAGE libraries, the truncated long SAGE libraries (tSAGE), from LongSAGE libraries by deleting seven 5' basepairs from each LongSAGE tag.