Hidden Markov Model

This Web Service implements SignalP v. 3.1. It predicts the presence and
location of signal peptide cleavage sites in amino acid sequences from
different organisms: Gram-positive prokaryotes, Gram-negative prokaryotes,
and eukaryotes. The method incorporates a prediction of cleavage sites
and a signal peptide/non-signal peptide prediction based on a combination
of several artificial neural networks and hidden Markov models. The method
is described in detail in the following article:

Improved prediction of signal peptides: SignalP 3.0.

TMHMM is a method for prediction transmembrane helices based on a hidden Markov model and
developed by Anders Krogh and Erik Sonnhammer. The method is described in detail in the
following articles:

Predicting transmembrane protein topology with a hidden Markov model: Application
to complete genomes. A. Krogh, B. Larsson, G. von Heijne, and E. L. L. Sonnhammer.
J. Mol. Biol., 305(3):567-580, January 2001.
PDF: http://www.binf.ku.dk/krogh/publications/pdf/KroghEtal01.pdf

A hidden Markov model for predicting transmembrane helices in protein sequences.

SHRIMP

This service allows the CATH and Gene3D HMM libraries to be searched with a given amino acid sequence (or any one of various database identifiers representing a sequence).

NB This service is not currently active. It is still in development -- check back here for updates. The WSDL posted may change between now and the first release.

The RNAmmer 1.2 server predicts 5s/8s, 16s/18s, and 23s/28s ribosomal RNA in full genome sequences. This page is the entry of the CBS Prediction Server for RNAmmer. RNAmmer is available also as a Web Service described by the following WSDL file. Please read the instructions on the RNAmmer Web Services section.
This pages allows academic users to download RNAmmer