Package: kebabs
Type: Package
Title: Kernel-Based Analysis Of Biological Sequences
Version: 1.6.2
Date: 2016-04-28
Author: Johannes Palme
Maintainer: Ulrich Bodenhofer <bodenhofer@bioinf.jku.at>
Description: The package provides functionality for kernel-based analysis of
DNA, RNA, and amino acid sequences via SVM-based methods. As core
functionality, kebabs implements following sequence kernels:
spectrum kernel, mismatch kernel, gappy pair kernel, and
motif kernel. Apart from an efficient implementation of standard
position-independent functionality, the kernels are extended in a
novel way to take the position of patterns into account for the
similarity measure. Because of the flexibility of the kernel
formulation, other kernels like the weighted degree kernel or
the shifted weighted degree kernel with constant weighting of
positions are included as special cases. An annotation-specific
variant of the kernels uses annotation information placed along
the sequence together with the patterns in the sequence.
The package allows for the generation of a kernel matrix or an
explicit feature representation in dense or sparse format for all
available kernels which can be used with methods implemented in
other R packages. With focus on SVM-based methods, kebabs
provides a framework which simplifies the usage of existing
SVM implementations in kernlab, e1071, and LiblineaR. Binary and
multi-class classification as well as regression tasks can be used
in a unified way without having to deal with the different
functions, parameters, and formats of the selected SVM. As support
for choosing hyperparameters, the package provides cross
validation - including grouped cross validation, grid search and
model selection functions. For easier biological interpretation of
the results, the package computes feature weights for all SVMs and
prediction profiles which show the contribution of individual
sequence positions to the prediction result and indicate the
relevance of sequence sections for the learning result and the
underlying biological functions.
URL: http://www.bioinf.jku.at/software/kebabs/
##Roxygen: list(wrap=TRUE)
License: GPL (>= 2.1)
Collate: AllClasses.R AllGenerics.R access-methods.R svmModel.R
kebabs.R kebabsData.R runtimeMessage.R parameters.R
sequenceKernel.R annotationSpecificKernel.R
positionDependentKernel.R spectrum.R mismatch.R gappyPair.R
motif.R explicitRepresentation.R coerce-methods.R
featureWeights.R heatmap-methods.R kbsvm-methods.R
performCrossValidation-methods.R gridSearch.R modelSelection.R
trainsvm-methods.R predictsvm-methods.R predict-methods.R
predictionProfile.R plot-methods.R kebabsDemo.R show-methods.R
symmetricPair.R svm.R utils.R zzz.R
Depends: R (>= 3.2.0), Biostrings (>= 2.35.5), kernlab
Imports: methods, stats, Rcpp (>= 0.11.2), Matrix, XVector (>= 0.7.3), S4Vectors (>= 0.5.11), e1071, LiblineaR, graphics, grDevices, utils, apcluster
LinkingTo: IRanges, XVector, Biostrings, Rcpp, S4Vectors
Suggests: SparseM, Biobase, BiocGenerics, knitr
VignetteBuilder: knitr
biocViews: SupportVectorMachine, Classification, Clustering, Regression
Packaged: 2016-05-16 05:07:15 UTC; biocbuild
NeedsCompilation: yes
Package: deepSNV
Maintainer: Moritz Gerstung <mg14@sanger.ac.uk>
License: GPL-3
Title: Detection of subclonal SNVs in deep sequencing data.
biocViews: GeneticVariability, SNP, Sequencing, Genetics, DataImport
LinkingTo: Rhtslib
Type: Package
LazyLoad: yes
Authors@R: c( person("Niko","Beerenwinkel", role="ths"),
person("David", "Jones", role = "ctb"),
person("Inigo", "Martincorena", role = "ctb"),
person("Moritz","Gerstung",
email = "mg14@sanger.ac.uk", role= c("aut","cre")) )
Description: This package provides provides quantitative variant callers for
detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing
experiments. The deepSNV algorithm is used for a comparative setup with a
control experiment of the same loci and uses a beta-binomial model and a
likelihood ratio test to discriminate sequencing errors and subclonal SNVs.
The shearwater algorithm computes a Bayes classifier based on a
beta-binomial model for variant calling with multiple samples for
precisely estimating model parameters such as local error rates and
dispersion and prior knowledge, e.g. from variation data bases such as
COSMIC.
Version: 1.18.1
URL: http://github.com/mg14/deepSNV
Depends: R (>= 2.13.0), methods, graphics, parallel, Rhtslib, IRanges, GenomicRanges, SummarizedExperiment, Biostrings, VGAM, VariantAnnotation (>= 1.13.44),
Imports: Rhtslib
Suggests: RColorBrewer, knitr
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2016-05-12 03:17:01 UTC; biocbuild
Author: Niko Beerenwinkel [ths],
David Jones [ctb],
Inigo Martincorena [ctb],
Moritz Gerstung [aut, cre]
Package: sangerseqR
Type: Package
Title: Tools for Sanger Sequencing Data in R
Version: 1.8.2
Date: 2014-07-17
Author: Jonathon T. Hill, Bradley Demarest
Maintainer: Jonathon Hill <jhill@byu.edu>
VignetteBuilder: knitr
Imports: methods, shiny
Depends: R (>= 3.0.2), Biostrings
Suggests: BiocStyle, knitr, RUnit, BiocGenerics
Description: This package contains several tools for analyzing Sanger
Sequencing data files in R, including reading .scf and .ab1
files, making basecalls and plotting chromatograms.
License: GPL-2
biocViews: Sequencing, SNP, Visualization
NeedsCompilation: no
Packaged: 2016-05-16 04:19:08 UTC; biocbuild
Package: scsR
Type: Package
Title: SiRNA correction for seed mediated off-target effect
Version: 1.8.0
Date: 2014-10-28
Author: Andrea Franceschini
Maintainer: Andrea Franceschini <andrea.franceschini@isb-sib.ch>, Roger Meier <roger.meier@lmsc.ethz.ch>, Christian von Mering <mering@imls.uzh.ch>
Description: Corrects genome-wide siRNA screens for seed mediated off-target effect. Suitable functions to identify the effective seeds/miRNAs and to visualize their effect are also provided in the package.
License: GPL-2
Depends: R (>= 2.14.0), STRINGdb, methods, BiocGenerics, Biostrings, IRanges, plyr, tcltk
Imports: sqldf, hash, ggplot2, graphics, grDevices, RColorBrewer
Suggests: RUnit
biocViews: Preprocessing
NeedsCompilation: no
Packaged: 2016-05-04 05:20:40 UTC; biocbuild
Package: seqbias
Version: 1.20.0
Date: 25-12-2010
Title: Estimation of per-position bias in high-throughput sequencing
data
Description: This package implements a model of per-position sequencing
bias in high-throughput sequencing data using a simple Bayesian
network, the structure and parameters of which are trained on a
set of aligned reads and a reference genome sequence.
Author: Daniel Jones <dcjones@cs.washington.edu>
Maintainer: Daniel Jones <dcjones@cs.washington.edu>
Depends: R (>= 2.13.0), GenomicRanges (>= 0.1.0), Biostrings (>=
2.15.0), methods
LinkingTo: Rsamtools (>= 1.19.38)
Imports: zlibbioc
Suggests: Rsamtools, ggplot2
LazyLoad: yes
License: LGPL-3
biocViews: Sequencing
NeedsCompilation: yes
Packaged: 2016-05-04 03:54:59 UTC; biocbuild
Package: systemPipeR
Type: Package
Title: systemPipeR: NGS workflow and report generation environment
Version: 1.6.2
Date: 2016-02-26
Author: Thomas Girke
Maintainer: Thomas Girke <thomas.girke@ucr.edu>
biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq,
RiboSeq, ChIPSeq, MethylSeq, SNP, GeneExpression, Coverage,
GeneSetEnrichment, Alignment, QualityControl
Description: R package for building and running automated end-to-end analysis workflows for a wide range of next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. Important features include a uniform workflow interface across different NGS applications, automated report generation, and support for running both R and command-line software, such as NGS aligners or peak/variant callers, on local computers or compute clusters. Efficient handling of complex sample sets and experimental designs is facilitated by a consistently implemented sample annotation infrastructure. Instructions for using systemPipeR are given in the Overview Vignette (HTML). The remaining Vignettes, linked below, are workflow templates for common NGS use cases.
Depends: Rsamtools, Biostrings, ShortRead, methods
Imports: BiocGenerics, GenomicRanges, GenomicFeatures, SummarizedExperiment, VariantAnnotation, rjson, ggplot2, grid, limma, edgeR, DESeq2, GOstats, GO.db, annotate, pheatmap, BatchJobs
Suggests: ape, RUnit, BiocStyle, knitr, rmarkdown, biomaRt, BiocParallel
VignetteBuilder: knitr
SystemRequirements: systemPipeR can be used to run external
command-line software (e.g. short read aligners), but the
corresponding tool needs to be installed on a system.
License: Artistic-2.0
URL: https://github.com/tgirke/systemPipeR
NeedsCompilation: no
Packaged: 2016-05-16 04:51:50 UTC; biocbuild
Package: triplex
Type: Package
Title: Search and visualize intramolecular triplex-forming sequences in
DNA
Version: 1.12.0
Date: 2013-09-28
Authors@R: c(person("Jiri", "Hon", role = c("aut", "cre"),
email = "jiri.hon@gmail.com"),
person("Matej", "Lexa", role = "aut",
email = "lexa@fi.muni.cz"),
person("Tomas", "Martinek", role = "aut",
email = "martinto@fit.vutbr.cz"),
person("Kamil", "Rajdl", role = "aut"),
person("Daniel", "Kopecek", role = "ctb"))
Author: Jiri Hon, Matej Lexa, Tomas Martinek and Kamil Rajdl with contributions from Daniel Kopecek
Maintainer: Jiri Hon <jiri.hon@gmail.com>
Description: This package provides functions for identification and
visualization of potential intramolecular triplex patterns in DNA sequence.
The main functionality is to detect the positions of subsequences capable of
folding into an intramolecular triplex (H-DNA) in a much larger sequence.
The potential H-DNA (triplexes) should be made of as many cannonical
nucleotide triplets as possible. The package includes visualization showing
the exact base-pairing in 1D, 2D or 3D.
License: BSD_2_clause + file LICENSE
URL: http://www.fi.muni.cz/~lexa/triplex/
biocViews: SequenceMatching, GeneRegulation
Depends: R (>= 2.15.0), S4Vectors (>= 0.5.14), IRanges (>= 2.5.27), XVector (>= 0.11.6), Biostrings (>= 2.39.10)
Imports: methods, grid, GenomicRanges
Suggests: rgl (>= 0.93.932), BSgenome.Celegans.UCSC.ce10, rtracklayer, GenomeGraphs
LinkingTo: S4Vectors, IRanges, XVector, Biostrings
NeedsCompilation: yes
Packaged: 2016-05-05 03:50:37 UTC; biocbuild
providing 
.