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TMHMM 2.0.ws0

Template service

NOTE: a newer version of this service (TMHMM 2.0.ws1) is available.

WSDL TMHMM/TMHMM_2_0_ws0.wsdl
Schema definitions ../common/ws_common_1_0b.xsd

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 downloaded free of charge from

Other versions and implementations

Ver.Last updated
2.0.ws1  2012-04-18(most recent)
2.0.ws0  2009-09-25(this version)
2.0  2007-04-01
2.0b  2007-10-02

Examples of client side scripts using the service

FilenameTypeCompatibilityAuthorDescription (3.0 KB) Perl 2.0 ws0 Edita Bartaseviciute
This script runs the TMHMM 2.0 ws0 Web Service. It reads FASTA file from STDIN and produces predictions in a simple table
(directory) (4.3 KB) Perl 2.0 ws0 Edita Bartaseviciute
This script runs the TMHMM 2.0 ws0 Web Service. It requires no input; to be used for testing in the EMBRACE WS Registry.
tmhmm_java (6.4 KB) Java 2.0 ws1 Karunakar Bayyapu
Client script in Java (apache) running the TMHMM Web Service
tmhmm_php (4.5 KB) PHP 2.0 ws1 Karunakar Bayyapu
Client script in PHP running the TMHMM Web Service
tmhmm_csharp (5.5 KB) C# 2.0 ws1 Karunakar Bayyapu
Client script in C# (mono) running the TMHMM Web Service
tmhmm_python (4.9 KB) Python 2.0 ws1 Karunakar Bayyapu
Client script in Python (suds) running the TMHMM Web Service
tmhmm_perl2 (5.4 KB) Perl 2.0 ws1 Karunakar Bayyapu
Client script in Perl (XML::Compile) running the TMHMM Web Service
example.fsa (1.3 KB)
check (0 B)
tmhmm_perl1 (5.5 KB) Perl 2.0 ws1 Karunakar Bayyapu
Client script in Perl (SOAP::Lite) running the TMHMM Web Service
tmhmm_soap_analysis (8.3 KB) 2.0 ws1 Karunakar Bayyapu
Detailed analysis of SOAP and WSDL versions with supported clients (1.9 KB) Perl NA Peter Fischer Hallin
Helper scripts used to initiate XML::Compile's proxys (WSDL+XSD)


# download the required scripts

perl < example.fsa 


          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.
          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 for details.


          This Web Service is fully asynchronous; the usage is split into the
          following three operations:.
          1.  runService    

              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

          2.  pollQueue

              Input:  Unique job identifier

              Output: 'jobstatus' - the status of the job
                        Possible values are QUEUED, ACTIVE, FINISHED, WAITING,
                        REJECTED, UNKNOWN JOBID or QUEUE DOWN

          3.  fetchResult

              Input :    Unique job identifier of a FINISHED job

              Output:    *  'annsource'
                            '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,; technical questions concerning
          the Web Service should go to Edita Bartaseviciute, or
          Kristoffer Rapacki,