Bias correction and Bayesian analysis of aggregate counts in SAGE libraries

Background: Tag-based techniques, such as SAGE, are commonly used to sample the mRNA pool of an organism's transcriptome. Incomplete digestion during the tag formation process may allow for multiple tags to be generated from a given mRNA transcript. The probability of forming a tag varies with its relative location. As a result, the observed tag counts represent a biased sample of the actual transcript pool. In SAGE this bias can be avoided by ignoring all but the 3' most tag but will discard a large fraction of the observed data. Taking this bias into account should allow more of the available data to be used leading to increased statistical power. Results: Three new hierarchical models, which directly embed a model for the variation in tag formation probability, are proposed and their associated Bayesian inference algorithms are developed. These models may be applied to libraries at both the tag and aggregate level. Simulation experiments and analysis of real data are used...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Russell L. Zaretzki, Michael A. Gilchrist, William M. Briggs, Artin Armagan
• Keywords: BMCBI 2010, Tag Formation, Tag Formation Probability, Tag Formation Process