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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("SimBindProfiles")
In most cases, you don't need to download the package archive at all.
SimBindProfiles
Similar Binding Profiles
Bioconductor version: 3.0
SimBindProfiles identifies common and unique binding regions in genome tiling array data. This package does not rely on peak calling, but directly compares binding profiles processed on the same array platform. It implements a simple threshold approach, thus allowing retrieval of commonly and differentially bound regions between datasets as well as events of compensation and increased binding.
Author: Bettina Fischer, Enrico Ferrero, Robert Stojnic, Steve Russell
Maintainer: Bettina Fischer <bef22 at cam.ac.uk>
Citation (from within R,
enter citation("SimBindProfiles")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("SimBindProfiles")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("SimBindProfiles")
| R Script | SimBindProfiles: Similar Binding Profiles, identifies common and unique regions in array genome tiling array data | |
| Reference Manual |
Details
| biocViews | Microarray, Software |
| Version | 1.4.0 |
| In Bioconductor since | BioC 2.13 (R-3.0) |
| License | GPL-3 |
| Depends | R (>= 2.10), methods, Ringo |
| Imports | limma, mclust, Biobase |
| LinkingTo | |
| Suggests | |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | SimBindProfiles_1.4.0.tar.gz |
| Windows Binary | SimBindProfiles_1.4.0.zip |
| Mac OS X 10.6 (Snow Leopard) | SimBindProfiles_1.4.0.tgz |
| Mac OS X 10.9 (Mavericks) | SimBindProfiles_1.4.0.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
- RNA-Seq workflow: gene-level exploratory analysis and differential expression
- Changing genomic coordinate systems with rtracklayer::liftOver
- Mass spectrometry and proteomics data analysis
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