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source("http://bioconductor.org/biocLite.R")
biocLite("segmentSeq")
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segmentSeq
Methods for identifying small RNA loci from high-throughput sequencing data
Bioconductor version: 3.0
High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods in this package allow the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery.
Author: Thomas J. Hardcastle
Maintainer: Thomas J. Hardcastle <tjh48 at cam.ac.uk>
Citation (from within R,
enter citation("segmentSeq")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("segmentSeq")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("segmentSeq")
| R Script | segmentSeq: small RNA locus detection | |
| R Script | segmentsSeq: Methylation locus identification | |
| Reference Manual |
Details
| biocViews | Alignment, DataImport, DifferentialExpression, MultipleComparison, QualityControl, Sequencing, Software |
| Version | 2.0.1 |
| In Bioconductor since | BioC 2.6 (R-2.11) |
| License | GPL-3 |
| Depends | R (>= 2.3.0), methods, baySeq(>= 1.99.0), ShortRead, GenomicRanges, IRanges, S4Vectors |
| Imports | graphics, grDevices, utils |
| LinkingTo | |
| Suggests | BiocStyle, BiocGenerics |
| 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 | segmentSeq_2.0.1.tar.gz |
| Windows Binary | segmentSeq_2.0.1.zip |
| Mac OS X 10.6 (Snow Leopard) | segmentSeq_2.0.1.tgz |
| Mac OS X 10.9 (Mavericks) | segmentSeq_2.0.1.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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