Statistical modeling of sequencing errors in SAGE libraries

Abstract

Abstract Motivation: Sequencing errors may bias the gene expression measurements made by Serial Analysis of Gene Expression (SAGE). They may introduce non-existent tags at low abundance and decrease the real abundance of other tags. These effects are increased in the longer tags generated in LongSAGE libraries. Current sequencing technology generates quite accurate estimates of sequencing error rates. Here we make use of the sequence neighborhood of SAGE tags and error estimates from the base-calling software to correct for such errors. Results: We introduce a statistical model for the propagation of sequencing errors in SAGE and suggest an Expectation-Maximizat..