2 The SAGE Methodology also introduces a posterior distribution which explicitly considers bias in tag formation and its consequences on inferences for expression rates. Section 4 introduces several approaches to simulate and estimate the posterior distribution. These include both MCMC based sam- pling methods as well as direct optimization of the posterior. Finally, Section 5 discusses implications of this method for analysis of SAGE and compares this methodology to that discussed by Morris et al. (2003). Further applications of this methodology to SAGE along with more general applications of the models are also discussed. The goal of a SAGE experiment is to sample the mRNA population within a group of cells and was developed by Velculescu et al. (1995). Broadly speaking, the SAGE method generates a set of short sequence-based cDNA tags from the mRNA population of a group of cells. Initially, a pool of mRNA is extracted from a group of cells. The unstable single stranded mRNA is reverse transcribed into a double stranded DNA copy (cDNA) using a modified primer that allows the cDNA to be bound to a streptavidin bead. The cDNA is cut into small ‘tags’, using restriction enzymes, whihc are concatenated together into longer cDNA molecules referred to as concatemers. These concatemers are then amplified and sequenced. A cleavage motif of the anchoring enzyme allows the start and stop points of tags to be identified in the sequence data. Each time a tag from a specific gene is observed in the sequence data, it contributes a count to the dataset. The data is then summarized by the number of tag counts for each individual gene within a genome, whihc are then used to make inferences about the mRNA population within a group of cells. The restriction enzymes used to create the tags only cut at very specific sites within the cDNA. For example, the restriction enzyme used by Velculescu et al. (1995) could only cleave the cDNA at the four nucleotide motif CATG. Thus, tags can only be created at specific 6