Digital Gene Expression of Rice

Abstract

Digital gene expression (DGE) technologies measure gene expression by counting sequence tags. As the cost of sequencing DNA decreases, the number of DGE datasets is expected to grow dramatically. There are now several variations on the technique, but essentially a DGE system can quantify a snapshot of gene expression, without the necessity of either knowing the gene sequence or designing probe sequences, as is needed for microarrays. This talk takes two rice DGE datasets as examples to reveal the statistical properties on analysis. The first is a 5-library SAGE (serial analysis of gene expression) including those from healthy leaves, bacteria blight-infected leaves, roots of seedlings, and seeds of 5 days after pollination (DAP) and of 10 DAP. The second is the MPSS (massively parallel signature sequencing) of 10 libraries including samples from anther, pollen, and embryo on different stages. Some statistical tests were applied to select those significantly differential expressed genes.