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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("clonotypeR")
In most cases, you don't need to download the package archive at all.
clonotypeR
High throughput analysis of T cell antigen receptor sequences
Bioconductor version: 3.0
High throughput analysis of T cell antigen receptor sequences The genes encoding T cell receptors are created by somatic recombination, generating an immense combination of V, (D) and J segments. Additional processes during the recombination create extra sequence diversity between the V an J segments. Collectively, this hyper-variable region is called the CDR3 loop. . The purpose of this package is to process and quantitatively analyse millions of V-CDR3-J combination, called clonotypes, from multiple sequence libraries.
Author: Charles Plessy <plessy at riken.jp>
Maintainer: Charles Plessy <plessy at riken.jp>
Citation (from within R,
enter citation("clonotypeR")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("clonotypeR")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("clonotypeR")
| HTML | R Script | clonotypeR User's Guide |
| Reference Manual | ||
| Text | README | |
| Text | NEWS | |
| Text | LICENSE |
Details
| biocViews | Sequencing, Software |
| Version | 1.4.0 |
| In Bioconductor since | BioC 2.13 (R-3.0) |
| License | file LICENSE |
| Depends | |
| Imports | methods |
| LinkingTo | |
| Suggests | BiocGenerics, edgeR, knitr, pvclust, RUnit, vegan |
| 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 | clonotypeR_1.4.0.tar.gz |
| Windows Binary | clonotypeR_1.4.0.zip |
| Mac OS X 10.6 (Snow Leopard) | clonotypeR_1.4.0.tgz |
| Mac OS X 10.9 (Mavericks) | clonotypeR_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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