Segmentation of the genome based on multiple samples of high-throughput sequencing data.

Description

The segmentSeq package is intended to take multiple samples of high-throughput data (together with replicate information) and identify regions of the genome which have a (reproducibly) high density of tags aligning to them. The package was developed for use in identifying small RNA precursors from small RNA sequencing data, but may also be useful in some mRNA-Seq and chIP-Seq applications.

Details

To use the package, we construct an alignmentData object from sets of alignment files using either the readGeneric function to read text files or the readBAM function to read from BAM format files.

We then use the processAD function to identify all potential subsegments of the data and the number of tags that align to these subsegments. We then use either a heuristic or empirical Bayesian approach to segment the genome into ‘loci’ and ‘null’ regions. We can then acquire posterior likelihoods for each set of replicates which tell us whether a region is likely to be a locus or a null in that replicate group.

The segmentation is designed to be usable by the baySeq package to allow differential expression analyses to be carried out on the discovered loci.

The package (optionally) makes use of the 'snow' package for parallelisation of computationally intensive functions. This is highly recommended for large data sets.

See the vignette for more details.

Author(s)

Thomas J. Hardcastle

Maintainer: Thomas J. Hardcastle <tjh48@cam.ac.uk>

References

Hardcastle T.J., Kelly, K.A. and Balcombe D.C. (2011). Identifying small RNA loci from high-throughput sequencing data. In press.

See Also

baySeq

Examples

# Define the chromosome lengths for the genome of interest.

chrlens <- c(2e6, 1e6)

# Define the files containing sample information.

datadir <- system.file("extdata", package = "segmentSeq")
libfiles <- c("SL9.txt", "SL10.txt", "SL26.txt", "SL32.txt")

# Establish the library names and replicate structure.

libnames <- c("SL9", "SL10", "SL26", "SL32")
replicates <- c(1,1,2,2)

# Process the files to produce an 'alignmentData' object.

alignData <- readGeneric(file = libfiles, dir = datadir, replicates =
replicates, libnames = libnames, chrs = c(">Chr1", ">Chr2"), chrlens =
chrlens)

# Process the alignmentData object to produce a 'segData' object.

sD <- processAD(alignData, gap = 100, cl = NULL)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(segmentSeq)
Loading required package: baySeq
Loading required package: GenomicRanges
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: abind
Loading required package: perm
Loading required package: ShortRead
Loading required package: BiocParallel
Loading required package: Biostrings
Loading required package: XVector
Loading required package: Rsamtools
Loading required package: GenomicAlignments
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/segmentSeq/segmentSeq-package.Rd_%03d_medium.png", width=480, height=480)
> ### Name: segmentSeq-package
> ### Title: Segmentation of the genome based on multiple samples of
> ###   high-throughput sequencing data.
> ### Aliases: segmentSeq-package segmentSeq
> ### Keywords: package
> 
> ### ** Examples
> 
> 
> # Define the chromosome lengths for the genome of interest.
> 
> chrlens <- c(2e6, 1e6)
> 
> # Define the files containing sample information.
> 
> datadir <- system.file("extdata", package = "segmentSeq")
> libfiles <- c("SL9.txt", "SL10.txt", "SL26.txt", "SL32.txt")
> 
> # Establish the library names and replicate structure.
> 
> libnames <- c("SL9", "SL10", "SL26", "SL32")
> replicates <- c(1,1,2,2)
> 
> # Process the files to produce an 'alignmentData' object.
> 
> alignData <- readGeneric(file = libfiles, dir = datadir, replicates =
+ replicates, libnames = libnames, chrs = c(">Chr1", ">Chr2"), chrlens =
+ chrlens)
Reading files........done!
Analysing tags...........done!
> 
> # Process the alignmentData object to produce a 'segData' object.
> 
> sD <- processAD(alignData, gap = 100, cl = NULL)
Chromosome: >Chr1
Finding start-stop co-ordinates...done!
1292 candidate loci found.
Chromosome: >Chr2
Finding start-stop co-ordinates...done!
13099 candidate loci found.
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>