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Bioinformatics Advance Access published online on June 9, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth342
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received February 23, 2004
Revised May 13, 2004
Accepted May 18, 2004

Article

EST clustering error evaluation and correction

Ji-Ping Z. Wang 1*, Bruce G. Lindsay 2, James Leebens-Mack 3, Liying Cui 3, Kerr Wall 3, Webb C. Miller 3, Claude W. dePamphilis 3

1 Department of Statistics, Northwestern University, Evanston, IL 60208; Department of Statistics, Pennsylvania State University, University Park, PA 16802
2 Department of Statistics, Pennsylvania State University, University Park, PA 16802
3 Department of Biology, Pennsylvania State University, University Park, PA 16802

* To whom correspondence should be addressed. E-mail: jzwang{at}northwestern.edu.


  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.

Availability: We have automated the methods developed in this paper in a web-based software ESTstat at http://cwdg5.bio.psu.edu/eststat. The supplemental data are available at the same website.


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