Package: rfPred
Type: Package
Title: Assign rfPred functional prediction scores to a missense
variants list
Version: 1.10.0
Date: 2013-07-17
Author: Fabienne Jabot-Hanin, Hugo Varet and Jean-Philippe Jais
Depends: Rsamtools, GenomicRanges, IRanges, data.table, methods, parallel
Suggests: BiocStyle
Maintainer: Hugo Varet <varethugo@gmail.com>
Description: Based on external numerous data files where rfPred scores
are pre-calculated on all genomic positions of the human exome,
the package gives rfPred scores to missense variants identified
by the chromosome, the position (hg19 version), the referent
and alternative nucleotids and the uniprot identifier of the
protein. Note that for using the package, the user has to be
connected on the Internet or to download the TabixFile and
index (approximately 3.3 Go).
License: GPL (>=2 )
URL: http://www.sbim.fr/rfPred
biocViews: Software, Annotation, Classification
NeedsCompilation: yes
Packaged: 2016-05-04 05:02:18 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: wavClusteR
Type: Package
Title: Sensitive and highly resolved identification of RNA-protein
interaction sites in PAR-CLIP data
Version: 2.6.2
Date: 2016-02-11
Depends: R (>= 3.2), GenomicRanges (>= 1.23.16), Rsamtools
Imports: methods, BiocGenerics, S4Vectors (>= 0.9.25), IRanges (>=
2.5.27), Biostrings, foreach, GenomicFeatures, ggplot2, Hmisc, mclust, rtracklayer, seqinr, stringr, wmtsa
Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19
Enhances: doMC
VignetteBuilder: knitr
Author: Federico Comoglio and Cem Sievers
Maintainer: Federico Comoglio <federico.comoglio@gmail.com>
Description: The package provides an integrated pipeline for the analysis
of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from
sequencing errors, SNPs and additional non-experimental sources by a non-
parametric mixture model. The protein binding sites (clusters) are then resolved
at high resolution and cluster statistics are estimated using a rigorous
Bayesian framework. Post-processing of the results, data export for UCSC genome
browser visualization and motif search analysis are provided. In addition, the
package allows to integrate RNA-Seq data to estimate the False Discovery Rate
of cluster detection. Key functions support parallel multicore computing. Note:
while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to
the analysis of other NGS data obtained from experimental procedures that induce
nucleotide substitutions (e.g. BisSeq).
License: GPL-2
biocViews: Sequencing, Technology, RIPSeq, RNASeq, Bayesian
LazyLoad: yes
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-05-16 04:35:06 UTC; biocbuild
Package: podkat
Type: Package
Title: Position-Dependent Kernel Association Test
Version: 1.4.2
Date: 2016-04-12
Author: Ulrich Bodenhofer
Maintainer: Ulrich Bodenhofer <bodenhofer@bioinf.jku.at>
Description: This package provides an association test that is capable
of dealing with very rare and even private variants. This is
accomplished by a kernel-based approach that takes the
positions of the variants into account. The test can be used
for pre-processed matrix data, but also directly for variant
data stored in VCF files. Association testing can be performed
whole-genome, whole-exome, or restricted to pre-defined regions
of interest. The test is complemented by tools for analyzing
and visualizing the results.
URL: http://www.bioinf.jku.at/software/podkat/
License: GPL (>= 2)
Depends: R (>= 3.2.0), methods, Rsamtools, GenomicRanges
Imports: Rcpp (>= 0.11.1), parallel, stats, graphics, grDevices, utils, Biobase, BiocGenerics, Matrix, GenomeInfoDb, IRanges, Biostrings, BSgenome (>= 1.32.0)
Suggests: BSgenome.Hsapiens.UCSC.hg38.masked, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Mmusculus.UCSC.mm10.masked, GWASTools (>= 1.13.24), VariantAnnotation, knitr
LinkingTo: Rcpp, Rsamtools
VignetteBuilder: knitr
Collate: AllGenerics.R AllClasses.R inputChecks.R sort-methods.R
show-methods.R print-methods.R summary-methods.R
p.adjust-methods.R c-methods.R access-methods.R
coerce-methods.R resampling.R unmaskedRegions.R
partitionRegions-methods.R genotypeMatrix-methods.R
computeKernel.R computePvalues.R readGenotypeMatrix-methods.R
readVariantInfo-methods.R readSampleNamesFromVcfHeader.R
readRegionsFromBedFile.R weightFuncs.R assocTest-methods.R
nullModel-methods.R qqplot-methods.R plot-methods.R
filterResult-methods.R split-methods.R computeWeights.R
weights-methods.R
biocViews: Genetics, WholeGenome, Annotation, VariantAnnotation,
Sequencing, DataImport
NeedsCompilation: yes
Packaged: 2016-05-16 05:21:25 UTC; biocbuild
Package: qrqc
Version: 1.26.0
Date: 2012-04-17
Title: Quick Read Quality Control
Author: Vince Buffalo
Maintainer: Vince Buffalo <vsbuffalo@ucdavis.edu>
Imports: reshape, ggplot2, Biostrings, biovizBase, graphics, methods, plyr, stats
Depends: reshape, ggplot2, Biostrings, biovizBase, brew, xtable, Rsamtools (>= 1.19.38), testthat
LinkingTo: Rsamtools
Description: Quickly scans reads and gathers statistics on base and
quality frequencies, read length, k-mers by position, and
frequent sequences. Produces graphical output of statistics for
use in quality control pipelines, and an optional HTML quality
report. S4 SequenceSummary objects allow specific tests and
functionality to be written around the data collected.
License: GPL (>=2)
URL: http://github.com/vsbuffalo/qrqc
biocViews: Sequencing, QualityControl, DataImport, Preprocessing,
Visualization
NeedsCompilation: yes
Packaged: 2016-05-04 04:01:43 UTC; biocbuild
providing 
.