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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("cleanUpdTSeq")
In most cases, you don't need to download the package archive at all.
cleanUpdTSeq
This package classifies putative polyadenylation sites as true or false/internally oligodT primed.
Bioconductor version: 2.14
This package uses the Naive Bayes classifier (from e1071) to assign probability values to putative polyadenylation sites (pA sites) based on training data from zebrafish. This will allow the user to separate true, biologically relevant pA sites from false, oligodT primed pA sites.
Author: Sarah Sheppard, Jianhong Ou, Nathan Lawson, Lihua Julie Zhu
Maintainer: Sarah Sheppard <Sarah.Sheppard at umassmed.edu>; Jianhong Ou <Jianhong.Ou at umassmed.edu>; Lihua Julie Zhu <Julie.Zhu at umassmed.edu>
Citation (from within R,
enter citation("cleanUpdTSeq")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("cleanUpdTSeq")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("cleanUpdTSeq")
| R Script | cleanUpdTSeq Vignette | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | GeneRegulation, Genetics, SequenceMatching, Sequencing, Software |
| Version | 1.2.0 |
| In Bioconductor since | BioC 2.13 (R-3.0) |
| License | GPL-2 |
| Depends | R (>= 2.15), BiocGenerics(>= 0.1.0), BSgenome, BSgenome.Drerio.UCSC.danRer7, GenomicRanges, seqinr, e1071 |
| Imports | |
| Suggests | |
| System Requirements | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | cleanUpdTSeq_1.2.0.tar.gz |
| Windows Binary | cleanUpdTSeq_1.2.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | cleanUpdTSeq_1.2.0.tgz |
| Mac OS X 10.9 (Mavericks) | cleanUpdTSeq_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
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