INTRODUCTION 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. 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. PDF: http://www.binf.ku.dk/krogh/publications/ps/SonnhammerEtal98.pdf Alongside this Web Service the TMHMM method is also implemented as a traditional click-and-paste WWW server at: http://www.cbs.dtu.dk/services/TMHMM/ 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: http://www.cbs.dtu.dk/cgi-bin/nph-sw_request?tmhmm Other users are requested to write to software@cbs.dtu.dk for details. WEB SERVICE OPERATION 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; 'annrecords/annrecord' 'feature feature name, here always 'TMHMM prediction'; 'range' 'begin' start position of the sequence range; 'end' end position of the sequence range; 'score' '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); CONTACT Questions concerning the scientific aspects of the TMHMM method should go to Anders Krogh, krogh@cbs.dtu.dk; technical questions concerning the Web Service should go to Karunakar Bayyapu, karun@cbs.dtu.dk or Kristoffer Rapacki, rapacki@cbs.dtu.dk.