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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("procoil")
In most cases, you don't need to download the package archive at all.
procoil
Prediction of Oligomerization of Coiled Coil Proteins
Bioconductor version: 3.0
The procoil package allows to predict whether a coiled coil sequence (amino acid sequence plus heptad register) is more likely to form a dimer or more likely to form a trimer. The predict function not only computes the prediction itself, but also a profile which allows to determine the strengths to which the individual residues are indicative for either class. Profiles can also be plotted and exported to files.
Author: Ulrich Bodenhofer
Maintainer: Ulrich Bodenhofer <bodenhofer at bioinf.jku.at>
Citation (from within R,
enter citation("procoil")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("procoil")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("procoil")
| R Script | PrOCoil - A Web Service and an R Package for Predicting the Oligomerization of Coiled-Coil Proteins | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Classification, Proteomics, Software |
| Version | 1.16.0 |
| In Bioconductor since | BioC 2.8 (R-2.13) |
| License | GPL (>= 2) |
| Depends | R (>= 2.12.0) |
| Imports | methods, stats, graphics |
| LinkingTo | |
| Suggests | Biostrings |
| SystemRequirements | |
| Enhances | |
| URL | http://www.bioinf.jku.at/software/procoil/ https://github.com/UBod/procoil |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | procoil_1.16.0.tar.gz |
| Windows Binary | procoil_1.16.0.zip |
| Mac OS X 10.6 (Snow Leopard) | procoil_1.16.0.tgz |
| Mac OS X 10.9 (Mavericks) | procoil_1.16.0.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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