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TMHMM 2.0.ws1

Template service

WSDL TMHMM/TMHMM_2_0_ws1.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(this version, most recent)
2.0.ws0  2009-09-25
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)


        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:.

    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,


        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 Karunakar Bayyapu, or
    Kristoffer Rapacki,