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.     

Serial analysis of gene expression: rapid RT-PCR analysis of unknown SAGE tags.

In a pilot study on SAGE on Reed-Sternberg cells we have sequenced 1055 tags representing 701 genes. Screening of the GenBank database resulted in the identification of a corresponding gene or EST for 490 of them. For 211 of the tags no homology could be detected. A major problem of the serial analysis of gene expression (SAGE) approach is how to further analyse the unknown tags. We have developed an RT-PCR-based method, rapid analysis of unknown SAGE tags (RAST-PCR), to analyse the expression of the corresponding genes. This approach can be used as a screening method to investigate whether or not the gene is differentially expressed between several cell types of interest.     

Identification and prevention of a GC content bias in SAGE libraries

Serial Analysis of Gene Expression (SAGE) is becoming a widely used gene expression profiling method for the study of development, cancer and other human diseases. Investigators using SAGE rely heavily on the quantitative aspect of this method for cataloging gene expression and comparing multiple SAGE libraries. We have developed additional computational and statistical tools to assess the quality and reproducibility of a SAGE library. Using these methods, a critical variable in the SAGE protocol was identified that has the potential to bias the Tag distribution relative to the GC content of the 10 bp SAGE Tag DNA sequence. We also detected this bias in a number of publicly available SAGE libraries. It is important to note that the GC content bias went undetected by quality control procedures in the current SAGE protocol and was only identified with the use of these statistical analyses on as few as 750 SAGE Tags. In addition to keeping any solution of free DiTags on ice, an analysis of the GC content should be performed before sequencing large numbers of SAGE Tags to be confident that SAGE libraries are free from experimental bias.     

Discarding duplicate ditags in LongSAGE analysis may introduce significant error

Background  

During gene expression analysis by Serial Analysis of Gene Expression (SAGE), duplicate ditags are routinely removed from the data analysis, because they are suspected to stem from artifacts during SAGE library construction. As a consequence, naturally occurring duplicate ditags are also removed from the analysis leading to an error of measurement.     

Analysis of allelic differential expression in the human genome using allele-specific serial analysis of gene expression tags

Recent reports have demonstrated that a significant proportion of human genes display allelic differential expression (ADE). ADE is associated with phenotypic variability and may contribute to complex genetic diseases. Here, we present a computational analysis of ADE using allele-specific serial analysis of gene expression (SAGE) tags representing 1295 human genes. We identified 472 genes for which unequal representation (>3-fold) of allele-specific SAGE tags was observed in at least one SAGE library, suggesting the occurrence of ADE. For 235 out of these 472 genes, the difference in the expression level between both allele-specific SAGE tags was statistically significant (p < 0.05). Eleven candidate genes were then subjected to experimental validation and ADE was confirmed for 8 out of these 11 genes. Our results suggest that at least 25% of the human genes display ADE and that allele-specific SAGE tags can be efficiently used for the identification of such genes.     

Accurate and unambiguous tag-to-gene mapping in serial analysis of gene expression

Background  

In this study, we present a robust and reliable computational method for tag-to-gene assignment in serial analysis of gene expression (SAGE). The method relies on current genome information and annotation, incorporation of several new features, and key improvements over alternative methods, all of which are important to determine gene expression levels more accurately. The method provides a complete annotation of potential virtual SAGE tags within a genome, along with an estimation of their confidence for experimental observation that ranks tags that present multiple matches in the genome.