Nucleic Acids Research, 2003, Vol. 31, No. 3 1067-1074
© 2003 Oxford University Press
A novel algorithm for computational identification of contaminated EST libraries
1 Compugen Ltd, 72 Pinchas Rosen Street, Tel Aviv 69512, Israel and 2 Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Ramat Aviv 69978, Israel
*To whom correspondence should be addressed at Compugen Ltd, 72 Pinchas Street, Tel Aviv 69512, Israel. Tel: +972 3765 8536; Fax: +972 3 765 8555; Email: rotem{at}compugen.co.il
Present address:
Hershel Safer, Zetiq Technologies Ltd, PO Box 2047, Ness Ziona 70400, Israel
A key goal of the Human Genome Project was to understand the complete set of human proteins, the proteome. Since the genome sequence by itself is not sufficient for predicting new genes and alternative splicing events that lead to new proteins, expressed sequence tags (ESTs) are used as the primary tool for these purposes. The high prevalence of artifacts in dbEST, however, often leads to invalid predictions. Here we describe a novel method for recognizing genomic DNA contamination and other artifacts that cannot be identified using current EST cleaning techniques. Our method uses the alignment of the entire set of ESTs to the human genome to identify highly contaminated EST libraries. We discovered 53 highly contaminated libraries and a subset of 24 766 ESTs from these libraries that probably represent contamination with genomic DNA, pre-mRNA, and ESTs that span non-canonical introns. Although this is only a small fraction of the entire EST dataset, each contaminating sequence could create a spurious transcript prediction. Indeed, in the clustering and assembly tool that we used, these sequences would have caused incorrect inference of 9575 new splice variants and 6370 new genes. Conclusions based on EST analysis, including prediction of alternative splicing, should be re-evaluated in light of these results. Our method, along with the identified set of contaminated sequences, will be essential for applications that depend on large EST datasets.
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