Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework

Background: Serial Analysis of Gene Expression (SAGE) is a high-throughput method for inferring mRNA expression levels from the experimentally generated sequence based tags. Standard analyses of SAGE data, however, ignore the fact that the probability of generating an observable tag varies across genes and between experiments. As a consequence, these analyses result in biased estimators and posterior probability intervals for gene expression levels in the transcriptome. Results: Using the yeast Saccharomyces cerevisiae as an example, we introduce a new Bayesian method of data analysis which is based on a model of SAGE tag formation. Our approach incorporates the variation in the probability of tag formation into the interpretation of SAGE data and allows us to derive exact joint and approximate marginal posterior distributions for the mRNA frequency of genes detectable using SAGE. Our analysis of these distributions indicates that the frequency of a gene in the tag pool is influenced ...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Michael A. Gilchrist, Hong Qin, Russell L. Zaretzki
• Keywords: BMCBI 2007, MRNA, SAGE Data, SAGE Tag Formation