Package: segmentSeq
Type: Package
Title: Methods for identifying small RNA loci from high-throughput
sequencing data
Version: 2.6.0
Date: 2010-01-20
Author: Thomas J. Hardcastle
Maintainer: Thomas J. Hardcastle <tjh48@cam.ac.uk>
Description: High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods in this package allow the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery.
License: GPL-3
LazyLoad: yes
Depends: R (>= 2.3.0), methods, baySeq (>= 1.99.0), ShortRead, GenomicRanges, IRanges, S4Vectors
Suggests: BiocStyle, BiocGenerics
Imports: graphics, grDevices, utils
biocViews: MultipleComparison, Sequencing, Alignment,
DifferentialExpression, QualityControl, DataImport
Packaged: 2016-05-04 04:02:57 UTC; biocbuild
NeedsCompilation: no
Package: Rcade
Title: R-based analysis of ChIP-seq And Differential Expression - a
tool for integrating a count-based ChIP-seq analysis with
differential expression summary data.
Version: 1.14.0
Date: 2012-01-25
Author: Jonathan Cairns
Maintainer: Jonathan Cairns <jmcairns200@gmail.com>
Description: Rcade (which stands for "R-based analysis of ChIP-seq And
Differential Expression") is a tool for integrating ChIP-seq
data with differential expression summary data, through a
Bayesian framework. A key application is in identifing the
genes targeted by a transcription factor of interest - that is,
we collect genes that are associated with a ChIP-seq peak, and
differential expression under some perturbation related to that
TF.
Depends: R (>= 2.14.0), methods, GenomicRanges, baySeq, Rsamtools
Imports: graphics, S4Vectors, rgl, plotrix
Suggests: limma, biomaRt, RUnit, BiocGenerics, BiocStyle
License: GPL-2
biocViews: DifferentialExpression, GeneExpression, Transcription,
ChIPSeq, Sequencing, Genetics
NeedsCompilation: no
Packaged: 2016-05-04 04:45:04 UTC; biocbuild
Package: TCC
Type: Package
Title: TCC: Differential expression analysis for tag count data with
robust normalization strategies
Version: 1.12.1
Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji
Kadota
Maintainer: Jianqiang Sun <wukong@bi.a.u-tokyo.ac.jp>, Tomoaki
Nishiyama <tomoakin@staff.kanazawa-u.ac.jp>
Description: This package provides a series of functions for performing
differential expression analysis from RNA-seq count data using
robust normalization strategy (called DEGES). The basic idea of
DEGES is that potential differentially expressed genes or
transcripts (DEGs) among compared samples should be removed
before data normalization to obtain a well-ranked gene list
where true DEGs are top-ranked and non-DEGs are bottom ranked.
This can be done by performing a multi-step normalization
strategy (called DEGES for DEG elimination strategy). A major
characteristic of TCC is to provide the robust normalization
methods for several kinds of count data (two-group with or
without replicates, multi-group/multi-factor, and so on) by
virtue of the use of combinations of functions in depended
packages.
Depends: R (>= 2.15), methods, DESeq, DESeq2, edgeR, baySeq, ROC
Imports: samr
Suggests: RUnit, BiocGenerics
Enhances: snow
License: GPL-2
Copyright: Authors listed above
biocViews: Sequencing, DifferentialExpression, RNASeq
NeedsCompilation: no
Packaged: 2016-05-27 04:20:14 UTC; biocbuild
Package: EDDA
Type: Package
Title: Experimental Design in Differential Abundance analysis
Version: 1.10.0
Date: 2015-01-06
Author: Li Juntao, Luo Huaien, Chia Kuan Hui Burton, Niranjan Nagarajan
Maintainer: Chia Kuan Hui Burton <chiakhb@gis.a-star.edu.sg>, Niranjan Nagarajan <nagarajann@gis.a-star.edu.sg>
Description: EDDA can aid in the design of a range of common experiments such as RNA-seq, Nanostring assays, RIP-seq and Metagenomic sequencing, and enables researchers to comprehensively investigate the impact of experimental decisions on the ability to detect differential abundance. This work was published on 3 December 2014 at Genome Biology under the title "The importance of study design for detecting differentially abundant features in high-throughput experiments" (http://genomebiology.com/2014/15/12/527).
License: GPL (>= 2)
Depends: Rcpp (>= 0.10.4), parallel, methods, ROCR, DESeq, baySeq, snow, edgeR
Imports: graphics, stats, utils, parallel, methods, ROCR, DESeq, baySeq, snow, edgeR
LinkingTo: Rcpp
biocViews: Sequencing, ExperimentalDesign, Normalization, RNASeq,
ChIPSeq
URL: http://edda.gis.a-star.edu.sg/
http://genomebiology.com/2014/15/12/527
NeedsCompilation: yes
Packaged: 2016-05-04 05:20:25 UTC; biocbuild
providing 
.