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: epigenomix
Type: Package
Title: Epigenetic and gene transcription data normalization and
integration with mixture models
Version: 1.12.0
Date: 2016-02-08
Author: Hans-Ulrich Klein, Martin Schaefer
Maintainer: Hans-Ulrich Klein <h.klein@uni-muenster.de>
Depends: R (>= 3.2.0), methods, Biobase, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment
Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb, beadarray
Description: A package for the integrative analysis of RNA-seq or
microarray based gene transcription and histone modification
data obtained by ChIP-seq. The package provides methods for
data preprocessing and matching as well as methods for fitting
bayesian mixture models in order to detect genes with
differences in both data types.
License: LGPL-3
biocViews: ChIPSeq, GeneExpression, DifferentialExpression,
Classification
NeedsCompilation: no
Packaged: 2016-05-04 04:53:55 UTC; biocbuild
Package: soGGi
Type: Package
Title: Visualise ChIP-seq, MNase-seq and motif occurrence as aggregate
plots Summarised Over Grouped Genomic Intervals
Version: 1.4.4
Date: 2015-12-02
Author: Gopuraja Dharmalingam, Tom Carroll
Maintainer: Tom Carroll <tc.infomatics@gmail.com>
Description: The soGGi package provides a toolset to create genomic
interval aggregate/summary plots of signal or motif occurence
from BAM and bigWig files as well as PWM, rlelist, GRanges and
GAlignments Bioconductor objects. soGGi allows for
normalisation, transformation and arithmetic operation on and
between summary plot objects as well as grouping and subsetting
of plots by GRanges objects and user supplied metadata. Plots
are created using the GGplot2 libary to allow user defined
manipulation of the returned plot object. Coupled together,
soGGi features a broad set of methods to visualise genomics
data in the context of groups of genomic intervals such as
genes, superenhancers and transcription factor binding events.
biocViews: Sequencing, ChIPSeq, Coverage
License: GPL (>= 3)
LazyLoad: yes
Depends: R (>= 3.2.0), BiocGenerics, SummarizedExperiment
Imports: methods, reshape2, ggplot2, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, preprocessCore, chipseq, BiocParallel
Collate: 'allClasses.r' 'motifTools.R' 'peakTransforms.r' 'plots.R'
'soggi.R'
VignetteBuilder: knitr
Suggests: testthat, BiocStyle, knitr
NeedsCompilation: no
Packaged: 2016-05-16 05:25:43 UTC; biocbuild
providing 
.