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Published online 27 February 2004

Nucleic Acids Research, 2004, Vol. 32, No. 4 1414-1421
© 2004 Oxford University Press

Identifying secretomes in people, pufferfish and pigs

Eric W. Klee1,3, Daniel F. Carlson4, Scott C. Fahrenkrug3,4, Stephen C. Ekker2,3 and Lynda B. M. Ellis*,1,3

1 Laboratory Medicine and Pathology, 2 Genetics, Cell Biology and Development, 3 Arnold and Mabel Beckman Center for Transposon Research, University of Minnesota, Minneapolis, MN 55455, USA and 4 Animal Science, University of Minnesota, St Paul, MN 55108, USA

*To whom correspondence should be addressed at: Mayo Mail Code 609, 420 SE Delaware Street, Minneapolis, MN 55455, USA. Tel: +1 612 625 9122; Fax: +1 612 624 6404; Email: lynda{at}umn.edu

The proteins processed by the secretory pathway (secretome) are critical players in the development of multi-cellular eukaryotic organisms but have yet to be comprehensively studied at the genomic level. In this study, we use the Target P algorithm to predict human (13–20% of proteins found in individual datasets) and Fugu (14%) secretomes based on analysis of their nearly complete proteomes. We combine internal processing with prediction software to automate secreted protein identification and overcome one of the major challenges associated with EST data: identification of the minority of clones that encode N-terminally-complete proteins. We discuss the use of these methods to predict secreted proteins in EST-based consensus sequence sets, and we validate these predictions using an assay for cell-free cotranslational translocation. Analysis of TIGR Porcine Gene Index 4.0 as a test dataset resulted in the identification of 352 N-terminally-complete, putative secreted proteins. In functional agreement with our predictions, 34 of 40 (85%) of these cDNAs were verified to be cotranslationally translocated in an in vitro translation system. The methods developed here are specifically designed to accept partial open reading frames and improve secreted protein predictions in eukaryotic transcriptomes, and are valuable for the analysis and annotation of eukaryotic EST databases.


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