Description Usage Arguments Details Value Note Author(s) References See Also Examples
Functions for reading a coiled coil prediction models
from a file into a CCModel
object
and writing a CCModel
object to a file.
1 2 | readCCModel(file)
writeCCModel(object, file)
|
file |
the name of the file from which |
object |
the |
The procoil package comes with two ready-made models
for oligomerization prediction, PrOCoilModel
and PrOCoilModelBA
. In case the user wants to
define custom models or wishes to use previous versions of
the prediction models, the functions readCCModel
and
writeCCModel
can be used to read/write models
from/to plain text files that can be viewed and also modified.
writeCCModel
writes models in the following format:
1 2 3 4 5 6 7 8 9 | _b,-1.07262284445085
_m,5
_scaling,1
L...Vd...a,1.63626232200227
R....Eg....e,1.5382098040217
R.Ec.e,1.29025032360792
E..Ve..a,1.22837780239385
...
|
Correspondingly, readCModel
expects the file to conform
to the above format. See CCModel
for an
overview of model parameters and an explanation of patterns and
weights.
Upon successful completion, readCCModel
returns a
CCModel
object. writeCCModel
returns an invisible NULL
.
The PrOCoil model is available on
on http://www.bioinf.jku.at/software/procoil/PrOCoilModel_v2.CCModel.
in exactly the format the function readCCModel
requires.
Analogously for the alternative model optimized for balanced
accuracy (see CCModel
):
http://www.bioinf.jku.at/software/procoil/PrOCoilModelBA_v2.CCModel.
The original models described in [Mahrenholz et al., 2011]
are available on
http://www.bioinf.jku.at/software/procoil/PrOCoilModel_v1.CCModel
and
http://www.bioinf.jku.at/software/procoil/PrOCoilModelBA_v1.CCModel,
respectively. So, by loading one of these files, the original models
can still be used.
Ulrich Bodenhofer bodenhofer@bioinf.jku.at
http://www.bioinf.jku.at/software/procoil/
Mahrenholz, C.C., Abfalter, I.G., Bodenhofer, U., Volkmer, R., and Hochreiter, S. (2011) Complex networks govern coiled coil oligomerization - predicting and profiling by means of a machine learning approach. Mol. Cell. Proteomics 10(5):M110.004994. DOI: 10.1074/mcp.M110.004994
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ## load small example model file for testing purposes
## NOTE: this is an incomplete model that will probably not provide
## meaningful predictions
file <- system.file("examples", "testModel.CCModel", package="procoil")
testModel <- readCCModel(file)
testModel
## Not run:
## read original model from file
URL <- "http://www.bioinf.jku.at/software/procoil/PrOCoilModel_v1.CCModel"
PrOCoilModelV1 <- readCCModel(URL)
## display summary of example model
PrOCoilModelV1
## display 10 heightes pattern weights
weights(PrOCoilModelV1)[1:10]
## End(Not run)
|
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