Package: consensusSeekeR
Version: 1.0.2
Date: 2015-05-01
Title: Detection of consensus regions inside a group of experiences
using genomic positions and genomic ranges
Description: This package compares genomic positions and genomic ranges from
multiple experiments to extract common regions. The size of the analyzed region
is adjustable as well as the number of experiences in which a feature must be
present in a potential region to tag this region as a consensus region.
Author: Astrid Deschenes [cre, aut], Fabien Claude Lamaze [ctb], Pascal Belleau
[aut], Arnaud Droit [aut]
Author@R: c(person("Astrid", "Deschenes", email="Astrid-
Louise.Deschenes@crchudequebec.ulaval.ca",
role=c("cre","aut")), person("Fabien Claude", "Lamaze",
email="fabien.lamaze.1@ulaval.ca", role=c("ctb")),
person("Pascal", "Belleau",
email="pascal.belleau@crchuq.ulaval.ca", role=c("aut")),
person("Arnaud", "Droit",
email="arnaud.droit@crchuq.ulaval.ca", role=c("aut")))
Depends: R (>= 2.10), BiocGenerics, IRanges, GenomicRanges, BiocParallel
Imports: GenomeInfoDb, rtracklayer, stringr, S4Vectors
Suggests: BiocStyle, ggplot2, knitr, RUnit
License: Artistic-2.0
URL: https://github.com/ArnaudDroitLab/consensusSeekeR
BugReports: https://github.com/ArnaudDroitLab/consensusSeekeR/issues
VignetteBuilder: knitr
NeedsCompilation: no
biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison,
Transcription, PeakDetection, Sequencing, Coverage
Maintainer: Astrid Louise Deschenes <Astrid-Louise.Deschenes@crchudequebec.ulaval.ca>
RoxygenNote: 5.0.1
Packaged: 2016-05-16 05:51:37 UTC; biocbuild
Package: copynumber
Title: Segmentation of single- and multi-track copy number data by
penalized least squares regression.
Version: 1.12.0
Author: Gro Nilsen, Knut Liestoel and Ole Christian Lingjaerde.
Maintainer: Gro Nilsen <gronilse@ifi.uio.no>
Description: Penalized least squares regression is applied to fit
piecewise constant curves to copy number data to locate genomic
regions of constant copy number. Procedures are available for
individual segmentation of each sample, joint segmentation of
several samples and joint segmentation of the two data tracks
from SNP-arrays. Several plotting functions are available for
visualization of the data and the segmentation results.
License: Artistic-2.0
LazyData: yes
Date: 2013-04-16
BuildResaveData: best
Depends: R (>= 2.10), BiocGenerics
Imports: S4Vectors, IRanges, GenomicRanges
biocViews: aCGH, SNP, CopyNumberVariation, Genetics, Visualization
NeedsCompilation: no
Packaged: 2016-05-04 04:55:17 UTC; biocbuild
Package: dexus
Type: Package
Title: DEXUS - Identifying Differential Expression in RNA-Seq Studies
with Unknown Conditions or without Replicates
Description: DEXUS identifies differentially expressed genes in RNA-Seq
data under all possible study designs such as studies without
replicates, without sample groups, and with unknown conditions.
DEXUS works also for known conditions, for example for RNA-Seq
data with two or multiple conditions. RNA-Seq read count data
can be provided both by the S4 class Count Data Set and by read
count matrices. Differentially expressed transcripts can be
visualized by heatmaps, in which unknown conditions,
replicates, and samples groups are also indicated. This
software is fast since the core algorithm is written in C. For
very large data sets, a parallel version of DEXUS is provided
in this package. DEXUS is a statistical model that is selected
in a Bayesian framework by an EM algorithm. DEXUS does not need
replicates to detect differentially expressed transcripts,
since the replicates (or conditions) are estimated by the EM
method for each transcript. The method provides an
informative/non-informative value to extract differentially
expressed transcripts at a desired significance level or power.
Version: 1.12.0
Date: 2015-01-27
Maintainer: Guenter Klambauer <klambauer@bioinf.jku.at>
Author: Guenter Klambauer
License: LGPL (>= 2.0)
Depends: R (>= 2.15), methods, BiocGenerics
Suggests: parallel, statmod, stats, DESeq, RColorBrewer
Collate: 'AllClasses.R' 'AllGenerics.R' 'binomTest.R' 'normalization.R'
'dexus.R' 'getSizeNB.R' 'functions.R' 'plot-methods.R'
'show-methods.R' 'methodsAccess.R' 'dexss.R'
biocViews: Sequencing, RNASeq, GeneExpression, DifferentialExpression,
CellBiology, Classification, QualityControl
NeedsCompilation: yes
Packaged: 2016-05-04 04:53:27 UTC; biocbuild
Package: ensemblVEP
Version: 1.12.0
Title: R Interface to Ensembl Variant Effect Predictor
Author: Valerie Obenchain
Maintainer: Bioconductor Package Maintainer <maintainer@bioconductor.org>
Depends: methods, BiocGenerics, GenomicRanges, VariantAnnotation
Imports: S4Vectors (>= 0.9.25), Biostrings, SummarizedExperiment
Suggests: RUnit
Description: Query the Ensembl Variant Effect Predictor via the perl API
SystemRequirements: Ensembl VEP (API version 84) and the Perl package
DBD::mysql must be installed. See the package README and
Ensembl web site,
http://www.ensembl.org/info/docs/tools/vep/index.html for
installation instructions.
License: Artistic-2.0
LazyLoad: yes
biocViews: Annotation, VariantAnnotation, SNP
NeedsCompilation: no
Packaged: 2016-05-04 04:49:45 UTC; biocbuild
Package: ensembldb
Type: Package
Title: Utilities to create and use an Ensembl based annotation database
Version: 1.4.6
Author: Johannes Rainer <johannes.rainer@eurac.edu>,
Tim Triche <tim.triche@usc.edu>
Maintainer: Johannes Rainer <johannes.rainer@eurac.edu>
URL: https://github.com/jotsetung/ensembldb
BugReports: https://github.com/jotsetung/ensembldb/issues
Imports: methods, RSQLite, DBI, Biobase, GenomeInfoDb, AnnotationDbi
(>= 1.31.19), rtracklayer, S4Vectors, AnnotationHub, Rsamtools, IRanges
Depends: BiocGenerics (>= 0.15.10), GenomicRanges (>= 1.23.21), GenomicFeatures (>= 1.23.18)
Suggests: BiocStyle, knitr, rmarkdown, EnsDb.Hsapiens.v75 (>= 0.99.7), RUnit, shiny, Gviz, BSgenome.Hsapiens.UCSC.hg19
VignetteBuilder: knitr
Description: The package provides functions to create and use
transcript centric annotation databases/packages. The
annotation for the databases are directly fetched from Ensembl
using their Perl API. The functionality and data is similar to
that of the TxDb packages from the GenomicFeatures package,
but, in addition to retrieve all gene/transcript models and
annotations from the database, the ensembldb package provides
also a filter framework allowing to retrieve annotations for
specific entries like genes encoded on a chromosome region or
transcript models of lincRNA genes.
Collate: Classes.R Generics.R dbhelpers.R Methods.R Methods-Filter.R
loadEnsDb.R makeEnsemblDbPackage.R EnsDbFromGTF.R runEnsDbApp.R
select-methods.R seqname-utils.R zzz.R
biocViews: Genetics, AnnotationData, Sequencing, Coverage
License: LGPL
NeedsCompilation: no
Packaged: 2016-06-07 05:12:18 UTC; biocbuild
Package: rsbml
Version: 2.30.0
Title: R support for SBML, using libsbml
Author: Michael Lawrence <michafla@gene.com>
Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils
Imports: BiocGenerics, graph, utils
SystemRequirements: libsbml (==5.10.2)
Maintainer: Michael Lawrence <michafla@gene.com>
Description: Links R to libsbml for SBML parsing, validating output,
provides an S4 SBML DOM, converts SBML to R graph objects.
Optionally links to the SBML ODE Solver Library (SOSLib) for
simulating models.
License: Artistic-2.0
URL: http://www.sbml.org
biocViews: GraphAndNetwork, Pathways, Network
NeedsCompilation: yes
Packaged: 2016-05-04 03:03:36 UTC; biocbuild
providing 
.