We recommend that the first time users should load the WSDL file above
to SoapUI and investigate the Web Service operations in that environment.
SoapUI is a desktop application for inspecting, invoking, developing and
functional/load/compliance testing of Web Services over HTTP. It can be
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Other versions and implementations
|2.0.ws1|| 2012-04-18||(this version, most recent)|
Examples of client side scripts using the service
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
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.
A hidden Markov model for predicting transmembrane helices in protein sequences.
E. L.L. Sonnhammer, G. von Heijne, and A. Krogh.
In J. Glasgow, T. Littlejohn, F. Major, R. Lathrop, D. Sankoff, and C. Sensen, editors,
Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology,
pages 175-182, Menlo Park, CA, 1998. AAAI Press.
Alongside this Web Service the TMHMM method is also implemented as
a traditional click-and-paste WWW server at:
TMHMM is also available as a stand-alone software package to install
and run at the user's site, with the same functionality. For academic
users there is a download page at:
Other users are requested to write to firstname.lastname@example.org for details.
WEB SERVICE OPERATION
This Web Service is fully asynchronous; the usage is split into the
following three operations:.
Input: The following parameters and data:
*'sequencedata' mulitple elements of type 'sequence':
*'sequence' answers to one sequence:
*'id' unique identifier for the sequence;
*'comment' optional comment;
*'seq' protein sequences. The sequences must be written
using the one letter amino acid code:
`acdefghiklmnpqrstvwy' or `ACDEFGHIKLMNPQRSTVWY'.
Other letters will be converted to `X' and treated
as unknown amino acids. Other symbols,
such as whitespace and numbers, will be ignored.
All the input sequences are truncated to 70 aa from
the N-terminal. Currently, at most 2.000 sequences
are allowed per submission.
Output: Unique job identifier
Input: Unique job identifier
Output: 'jobstatus' - the status of the job
Possible values are QUEUED, ACTIVE, FINISHED, WAITING,
REJECTED, UNKNOWN JOBID or QUEUE DOWN
Input : Unique job identifier of a FINISHED job
'method' name of the method, here always TMHMM;
'version' version of the methods, here always 2.0 ws0;
'ann' annotations - one element per input sequence;
'sequence' standard sequence object;
'id' sequence identifier;
'feature feature name, here always 'TMHMM prediction';
'begin' start position of the sequence range;
'end' end position of the sequence range;
'key'='no_score' the predictor gives the most probable
location and orientation of transmembrane
helices in the sequence and doesn't produce
any numerical score;
'value' the score value here is always 0.0 as the method
doesn't produce any score;
'comment' location of the sequence range
(inside, outside or TMhelix);
Questions concerning the scientific aspects of the TMHMM method should
go to Anders Krogh, email@example.com; technical questions concerning
the Web Service should go to Karunakar Bayyapu, firstname.lastname@example.org or
Kristoffer Rapacki, email@example.com.