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SecretomeP 2.0 Server

Prediction of non-classical protein secretion

The SecretomeP 2.0 server produces ab initio predictions of non-classical i.e. not signal peptide triggered protein secretion. The method queries a large number of other feature prediction servers to obtain information on various post-translational and localizational aspects of the protein, which are integrated into the final secretion prediction.

View the version history of this server.

Instructions Output format Article abstracts Data sets Supplementary material

SUBMISSION

Paste a single sequence or several sequences in FASTA format into the field below:

Submit a file in FASTA format directly from your local disk:

Gram-negative bacteria
Gram-positive bacteria
Mammalian

Restrictions:
At most 100 sequences and 200,000 amino acids per submission; each sequence not less than 40 and not more than 4,000 amino acids.

Confidentiality:
The sequences are kept confidential and will be deleted after processing.

CITATIONS

For publication of results, please cite as follows. When using predictions for mammalian sequences:

Feature based prediction of non-classical and leaderless protein secretion
J. Dyrløv Bendtsen, L. Juhl Jensen, N. Blom, G. von Heijne and S. Brunak
Protein Eng. Des. Sel., 17(4):349-356, 2004

View the abstract.

When using predictions for bacterial sequences:

Non-classical protein secretion in bacteria
J. Dyrløv Bendtsen, L. Kiemer, A. Fausbøll and S. Brunak
BMC Microbiology, 5:58, 2005

View the abstract.

PORTABLE VERSION

Would you prefer to run SecretomeP at your own site? SecretomeP 1.0 is available as a stand-alone software package, with the same functionality as ver. 1.0 on the WWW (for eukaryotes only). Ready-to-ship packages exist for the most common UNIX platforms. There is a download page for academic users; other users are requested to contact CBS Software Package Manager at software@cbs.dtu.dk.



GETTING HELP

Scientific problems:        Technical problems:       

This file was last modified Monday 31st 2017f July 2017 11:57:46 GMT