To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("Rcpi")
In most cases, you don't need to download the package archive at all.
Rcpi
Toolkit for Compound-Protein Interaction in Drug Discovery
Bioconductor version: 3.0
The Rcpi package offers an R/Bioconductor package emphasizing the comprehensive integration of bioinformatics and chemoinformatics into a molecular informatics platform for drug discovery.
Author: Nan Xiao <road2stat at gmail.com>, Dongsheng Cao <oriental-cds at 163.com>, Qingsong Xu <dasongxu at gmail.com>
Maintainer: Nan Xiao <road2stat at gmail.com>
Citation (from within R,
enter citation("Rcpi")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("Rcpi")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("Rcpi")
| R Script | Rcpi Quick Reference Card | |
| R Script | Rcpi: R/Bioconductor Package as an Integrated Informatics Platform in Drug Discovery | |
| Reference Manual | ||
| Text | README | |
| Text | NEWS |
Details
| biocViews | BiomedicalInformatics, Cheminformatics, DataImport, DataRepresentation, FeatureExtraction, GO, GraphAndNetwork, Proteomics, Software, SystemsBiology |
| Version | 1.2.0 |
| In Bioconductor since | BioC 2.14 (R-3.1) |
| License | Artistic-2.0 |
| Depends | |
| Imports | RCurl, rjson, rcdk, foreach, doParallel, Biostrings, GOSemSim, ChemmineR, fmcsR |
| LinkingTo | |
| Suggests | RUnit, BiocGenerics |
| SystemRequirements | |
| Enhances | ChemmineOB |
| URL | https://github.com/road2stat/Rcpi |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | Rcpi_1.2.0.tar.gz |
| Windows Binary | Rcpi_1.2.0.zip |
| Mac OS X 10.6 (Snow Leopard) | Rcpi_1.2.0.tgz |
| Mac OS X 10.9 (Mavericks) | Rcpi_1.2.0.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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