Bioconductor version: 2.9
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>
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R") biocLite("segmentSeq")
To cite this package in a publication, start R and enter:
citation("segmentSeq")
R Script | segmentSeq | |
Reference Manual |
biocViews | Bioinformatics, HighThroughputSequencing, MultipleComparisons |
Depends | R (>= 2.3.0), methods, baySeq(>= 1.8.1), ShortRead, GenomicRanges, IRanges |
Imports | baySeq, graphics, grDevices, IRanges, methods, utils, GenomicRanges |
Suggests | snow |
System Requirements | |
License | GPL-3 |
URL | |
Depends On Me | |
Imports Me | |
Suggests Me | |
Version | 1.6.2 |
Since | Bioconductor 2.6 (R-2.11) |
Package Source | segmentSeq_1.6.2.tar.gz |
Windows Binary | segmentSeq_1.6.2.zip (32- & 64-bit) |
MacOS 10.5 (Leopard) binary | segmentSeq_1.6.2.tgz |
Package Downloads Report | Download Stats |
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