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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("RIPSeeker")
In most cases, you don't need to download the package archive at all.
RIPSeeker
RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments
Bioconductor version: 2.14
Infer and discriminate RIP peaks from RIP-seq alignments using two-state HMM with negative binomial emission probability. While RIPSeeker is specifically tailored for RIP-seq data analysis, it also provides a suite of bioinformatics tools integrated within this self-contained software package comprehensively addressing issues ranging from post-alignments processing to visualization and annotation.
Author: Yue Li
Maintainer: Yue Li <yueli at cs.toronto.edu>
Citation (from within R,
enter citation("RIPSeeker")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("RIPSeeker")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("RIPSeeker")
| R Script | RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | RIPSeq, Sequencing, Software |
| Version | 1.4.0 |
| In Bioconductor since | BioC 2.12 (R-3.0) |
| License | GPL-2 |
| Depends | R (>= 2.15), methods, IRanges, GenomicRanges, Rsamtools, GenomicAlignments, rtracklayer |
| Imports | |
| Suggests | biomaRt, ChIPpeakAnno, parallel, GenomicFeatures |
| System Requirements | |
| URL | http://www.cs.utoronto.ca/~yueli/software.html |
| Depends On Me | RIPSeekerData |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | RIPSeeker_1.4.0.tar.gz |
| Windows Binary | RIPSeeker_1.4.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | RIPSeeker_1.4.0.tgz |
| Mac OS X 10.9 (Mavericks) | RIPSeeker_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
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