Abstract

Motivation: The gene expression intensity information conveyed by EST data can be used to infer important cDNA library properties, such as gene number and expression patterns (Audic and Claverie, 1997; Stekel et al., 2000). However, EST clustering errors, which often lead to greatly inflated estimates of obtained unique genes, have become a major obstacle in the analyses. The EST clustering error structure, the relationship between clustering error and clustering criteria, and possible error correction methods need to be systematically investigated. Results: We identify and quantify two types of EST clustering error, namely, Type I and II in EST clustering using CAP3 assembling program. A Type I error occurs when ESTs from the same gene do not form a cluster whereas a Type II error occurs when ESTs from distinct genes are falsely clustered together. While the Type II error rate is less than 1.5 % for both 5 ′ and 3 ′ EST clustering, the Type I error in the 5 ′ EST case is about 10 times higher than the 3 ′ EST case (30 % vs 3%). An over-stringent identity rule, for example, P ≥ 95%, may even inflate the Type I error in both cases. We demonstrate that about 80 % of the Type I error is due to insufficient overlap among sibling ESTs (ISO error) in 5 ′ EST clustering. A novel statistical approach is proposed to correct ISO error to provide more accurate estimates of the true gene cluster profile.

Citations