Web Service WSTMHMM_2_0b SOAP Dk

Cog_error Archived / Deprecated

This service has been archived because it may not be active anymore (or is close to being non active). Please do not use it as it may not be accessible.

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Annotations: 3 Total number of annotations from the provider's definition / description document. 1 Total number of annotations submitted by members of BioCatalogue. 0 Total number of annotations sourced from other registries. 2


Center for Biological Sequence Analysis (CBS)

Denmark Dk

SeekDa (over 11 years ago)

Base URL:

WSDL Location:
http://pigdb.org/ws/TMHMM/TMHMM_2_0b.wsdl (download last cached WSDL file)

Documentation URL(s):
from provider's description doc (over 11 years ago)
		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:


	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 software@cbs.dtu.dk 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:
	        'graphics'     OPTIONAL. Can be 'yes' or 'no' indicating whether or not
	                       graphical output should be added to the output. PLEASE BE AWARE
	                       that this option adds significant compute time and it is therefor 
	                       advised to apply this to smaller datasets (50-100 proteins). For 
	                       larger data sets, an initial run can be submitted without graphical
	                       output, and a job can subsequently be submitted based on the filtered 
	                       results for the first run, now including graphics.
	        'sequences'    protein sequences, with unique identifiers (mandatory) 
	                       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,

	3. fetchResult

	Input:  Unique job identifier of a FINISHED job

	Output: 'output' - prediction results:

	        For each input sequence a record is output consisting of the
	        following fields:

	        'len'          The length of the protein sequence
	        'PredHel'      The number of predicted transmembrane helices.
	        'ExpAA'        The expected number of amino acids intransmembrane helices. 
	                       If this number is larger than 18 it is very likely to be a 
	                       transmembrane protein (OR have a signal peptide).
	        'First60'      The expected number of amino acids in transmembrane helices 
	                       in the first 60 amino acids of the protein. If this number 
	                       more than a few, you should be warned that a predicted
	                       transmembrane helix in the N-term could be a signal peptide.
	        'NinProb'      The total probability that the N-term is on the cytoplasmic side 
	                       of the membrane.
	        'NtermSignal'  (yes/no) A warning that is produced when 'First60' is larger than 10.
	        'image'        OPTIONAL - common image data type, base64 encoded PNG image 
	                       'comment'     Fixed: Posterior probabilities of inside/outside/TM helix
	                       'encoding'    Fixed: base64
	                       'MIMEtype'    Fixed: image/png
	                       'content'     Base64 encoded image: iVBORw0KGgoAAAANS...
	        'topology'     The topology of the helix prediction:
	                      'location'   (inside/outside)
	                      'begin'      Start postion
	                      'end'        End position

2.0  : initial
2.0b : A change was made to version 2.0b to use a different service
       endpoint (simple.cgi instead of server.cgi). The change is transparent to the 
       to the user. 


	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 Peter Fischer Hallin, pfh@cbs.dtu.dk or
	Kristoffer Rapacki, rapacki@cbs.dtu.dk.
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