WSTMHMM_2_0

By | September 22, 2011

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:
‘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,
REJECTED, UNKNOWN JOBID or QUEUE DOWN

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

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

Name
WSTMHMM_2_0
Documentation
Protocol
SOAP
WSDL
Endpoint
http://ws.cbs.dtu.dk/cgi-bin/soap/ws/server.cgi
Topic
Protein Structure Prediction
Type
Analysis
Tags
Description

TMHMM is a method for prediction transmembrane helices based on a hidden Markov model and developed by Anders Krogh and [...]

Further information

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:
‘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,
REJECTED, UNKNOWN JOBID or QUEUE DOWN

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

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

Original source
BioCatalogue

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