Description Usage Arguments Details Value Author(s) References See Also Examples
Create a motif kernel object and the kernel matrix
1 2 3 4 5  motifKernel(motifs, r = 1, annSpec = FALSE, distWeight = numeric(0),
normalized = TRUE, exact = TRUE, ignoreLower = TRUE, presence = FALSE)
## S4 method for signature 'MotifKernel'
getFeatureSpaceDimension(kernel, x)

motifs 
a set of motif patterns specified as character vector. The order in which the patterns are passed for creation of the kernel object also determines the order of the features in the explicit representation. Lowercase characters in motifs are always converted to uppercase. For details concerning the definition of motif patterns see below and in the examples section. 
r 
exponent which must be > 0 (see details section in spectrumKernel). Default=1 
annSpec 
boolean that indicates whether sequence annotation should
be taken into account (details see on help page for

distWeight 
a numeric distance weight vector or a distance weighting
function (details see on help page for 
normalized 
generated data from this kernel will be normalized (details see below). Default=TRUE 
exact 
use exact character set for the evaluation (details see below). Default=TRUE 
ignoreLower 
ignore lower case characters in the sequence. If the parameter is not set lower case characters are treated like uppercase. default=TRUE 
presence 
if this parameter is set only the presence of a motif will be considered, otherwise the number of occurances of the motif is used; Default=FALSE 
kernel 
a sequence kernel object 
x 
one or multiple biological sequences in the form of a

Creation of kernel object
The function 'motif' creates a kernel object for the motif kernel for a set
of given DNA, RNA or AAmotifs. This kernel object can then be used to
generate a kernel matrix or an explicit representation for this kernel.
The individual patterns in the set of motifs are built similar to regular
expressions through concatination of following elements in arbitrary order:
a specific character from the used character set  e.g. 'A' or 'G' in DNA patterns for matching a specific character
the wildcard character '.' which matches any valid character of the character set except ''
a substitution group specified by a collection of characters from the character set enclosed in square brackets  e.g. [AG]  which matches any of the listed characters; with a leading '^' the character list is inverted and matching occurs for all characters of the character set which are not listed except ''
For values different from 1 (=default value) parameter r
leads
to a transfomation of similarities by taking each element of the similarity
matrix to the power of r. For the annotation specific variant of this
kernel see annotationMetadata, for the distance weighted
variants see positionMetadata. If normalized=TRUE
, the
feature vectors are scaled to the unit sphere before computing the
similarity value for the kernel matrix. For two samples with the feature
vectors x
and y
the similarity is computed as:
s=(x^T y)/(x y)
For an explicit representation generated with the feature map of a
normalized kernel the rows are normalized by dividing them through their
Euclidean norm. For parameter exact=TRUE
the sequence characters
are interpreted according to an exact character set. If the flag is not
set ambigous characters from the IUPAC characterset are also evaluated.
The annotation specific variant (for details see annotationMetadata) and the position dependent variants (for details see positionMetadata) either in the form of a position specific or a distance weighted kernel are supported for the motif kernel. The generation of an explicit representation is not possible for the position dependent variants of this kernel.
Hint: For a normalized motif kernel with a feature subset of a normalized
spectrum kernel the explicit representation will not be identical to the
subset of an explicit representation for the spectrum kernel because
the motif kernel is not aware of the other kmers which are used in the
spectrum kernel additionally for normalization.
Creation of kernel matrix
The kernel matrix is created with the function getKernelMatrix
or via a direct call with the kernel object as shown in the examples below.
motif: upon successful completion, the function returns a kernel
object of class MotifKernel
.
of getDimFeatureSpace: dimension of the feature space as numeric value
Johannes Palme <[email protected]>
http://www.bioinf.jku.at/software/kebabs
(BenHur, 2003) – A. BenHur, and D. Brutlag. Remote homology detection:
a motif based approach.
(Bodenhofer, 2009) – U. Bodenhofer, K. Schwarzbauer, M. Ionescu and
S. Hochreiter. Modelling position specificity in sequence kernels by fuzzy
equivalence relations.
(Mahrenholz, 2011) – C.C. Mahrenholz, I.G. Abfalter, U. Bodenhofer, R. Volkmer
and S. Hochreiter. Complex networks govern coiledcoil oligomerizations 
predicting and profiling by means of a machine learning approach.
J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
for kernelbased analysis of biological sequences.
Bioinformatics, 31(15):25742576, 2015.
DOI: 10.1093/bioinformatics/btv176.
kernelParametersmethod
,
getKernelMatrix
, getExRep
,
spectrumKernel
, mismatchKernel
,
gappyPairKernel
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33  ## instead of user provided sequences in XStringSet format
## for this example a set of DNA sequences is created
## RNA or AAsequences can be used as well with the motif kernel
dnaseqs < DNAStringSet(c("AGACTTAAGGGACCTGGTCACCACGCTCGGTGAGGGGGACGGGGTGT",
"ATAAAGGTTGCAGACATCATGTCCTTTTTGTCCCTAATTATTTCAGC",
"CAGGAATCAGCACAGGCAGGGGCACGGCATCCCAAGACATCTGGGCC",
"GGACATATACCCACCGTTACGTGTCATACAGGATAGTTCCACTGCCC",
"ATAAAGGTTGCAGACATCATGTCCTTTTTGTCCCTAATTATTTCAGC"))
names(dnaseqs) < paste("S", 1:length(dnaseqs), sep="")
## create the kernel object with the motif patterns
mot < motifKernel(c("A[CG]T","C.G","G[^A][AT]"), normalized=FALSE)
## show details of kernel object
mot
## generate the kernel matrix with the kernel object
km < mot(dnaseqs)
dim(km)
km
## alternative way to generate the kernel matrix
km < getKernelMatrix(mot, dnaseqs)
## Not run:
## plot heatmap of the kernel matrix
heatmap(km, symm=TRUE)
## generate rectangular kernel matrix
km < mot(x=dnaseqs, selx=1:3, y=dnaseqs, sely=4:5)
dim(km)
km
## End(Not run)

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