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.     

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.     

Gene Class expression: analysis tool of Gene Ontology terms with gene expression data

Serial analysis of gene expression (SAGE) technology produces large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in these gene sets. We present an interactive web-based tool, called Gene Class, which allows functional annotation of SAGE data using the Gene Ontology (GO) database. This tool performs searches in the GO database for each SAGE tag, making associations in the selected GO category for a level selected in the hierarchy. This system provides user-friendly data navigation and visualization for mapping SAGE data onto the gene ontology structure. This tool also provides graphical visualization of the percentage of SAGE tags in each GO category, along with confidence intervals and hypothesis testing.     

Serial analysis of gene expression in the silkworm, Bombyx mori

The silkworm Bombyx mori is one of the most economically important insects and serves as a model for Lepidoptera insects. We used serial analysis of gene expression (SAGE) to derive profiles of expressed genes during the developmental life cycle of the silkworm and to create a reference for understanding silkworm metamorphosis. We generated four SAGE libraries, one from each of the four developmental stages of the silkworm. In total we obtained 257,964 SAGE tags, of which 39,485 were unique tags. Sorted by copy number, 14.1% of the unique tags were detected at a median to high level (five or more copies), 24.2% at lower levels (two to four copies), and 61.7% as single copies. Using a basic local alignment search tool on the EST database, 35% of the tags matched known silkworm expressed sequence tags. SAGE demonstrated that a number of the genes were up- or down-regulated during the four developmental phases of the egg, larva, pupa, and adult. Furthermore, we found that the generation of longer cDNA fragments from SAGE tags constituted the most efficient method of gene identification, which facilitated the analysis of a large number of unknown genes.