Package 'RJMCMCNucleosomes' reference manual

Title:	Bayesian hierarchical model for genome-wide nucleosome positioning with high-throughput short-read data (MNase-Seq)
Description:	This package does nucleosome positioning using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling.
Authors:	Pascal Belleau [aut], Rawane Samb [aut], Astrid Deschênes [cre, aut], Khader Khadraoui [aut], Lajmi Lakhal-Chaieb [aut], Arnaud Droit [aut]
Maintainer:	Astrid Deschênes <[email protected]>
License:	Artistic-2.0
Version:	1.29.0
Built:	2024-06-30 05:44:21 UTC
Source:	https://github.com/bioc/RJMCMCNucleosomes

RJMCMCNucleosomes: Bayesian hierarchical model for genome-wide nucleosome positioning with high-throughput short-read data (MNase-Seq)

Description

This package does nucleosome positioning using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling.

Author(s)

Pascal Belleau, Rawane Samb, Astrid Deschênes, Khader Khadraoui, Lajmi Lakhal and Arnaud Droit

Maintainer: Astrid Deschenes <[email protected]>

Merge nucleosome information from all RDS files present in a same directory. Beware that only nucleosome information from same chromosome should be merged together.

Description

Merge nucleosome information, from all RDS files present in a same directory, into one object of class "rjmcmcNucleosomesMerge".

Usage

mergeAllRDSFilesFromDirectory(directory)
mergeAllRDSFilesFromDirectory(directory)

Arguments

directory

a character, the name of the directory (relative or absolute path) containing RDS files. The RDS files must contain R object of class "rjmcmcNucleosomes" or "rjmcmcNucleosomesMerge".

Value

a list of class "rjmcmcNucleosomesMerge" containing:

k a integer, the number of nucleosomes.
mu a GRanges containing the positions of the nucleosomes.

Author(s)

Pascal Belleau, Astrid Deschenes

Examples


## Use a directory present in the RJMCMC package
directoryWithRDSFiles <- system.file("extdata",
package = "RJMCMCNucleosomes")

## Merge nucleosomes info from RDS files present in directory
## It is assumed that all files present in the directory are nucleosomes
## result for the same chromosome
result <- mergeAllRDSFilesFromDirectory(directoryWithRDSFiles)

## Print the number and the position of the nucleosomes
result$k
result$mu

## Class of the output object
class(result)


## Use a directory present in the RJMCMC package
directoryWithRDSFiles <- system.file("extdata",
package = "RJMCMCNucleosomes")

## Merge nucleosomes info from RDS files present in directory
## It is assumed that all files present in the directory are nucleosomes
## result for the same chromosome
result <- mergeAllRDSFilesFromDirectory(directoryWithRDSFiles)

## Print the number and the position of the nucleosomes
result$k
result$mu

## Class of the output object
class(result)

Merge nucleosome information from selected RDS files.

Description

Merge nucleosome information present in RDS files into one object of class "rjmcmcNucleosomesMerge".

Usage

mergeRDSFiles(RDSFiles)
mergeRDSFiles(RDSFiles)

Arguments

RDSFiles

a array, the names of all RDS used to merge nucleosome information. The files must contain R object of class "rjmcmcNucleosomes" or "rjmcmcNucleosomesMerge".

Value

a list of class "rjmcmcNucleosomesMerge" containing:

k a integer, the number of nucleosomes.
mu a GRanges containing the positions of the nucleosomes.

Author(s)

Pascal Belleau, Astrid Deschenes

Examples


## Use RDS files present in the RJMCMC package
RDSFiles <- dir(system.file("extdata", package = "RJMCMCNucleosomes"),
full.names = TRUE, pattern = "*RDS")

## Merge nucleosomes info from RDS files present in directory
result <- mergeRDSFiles(RDSFiles)

## Print the number and the position of the nucleosomes
result$k
result$mu

## Class of the output object
class(result)


## Use RDS files present in the RJMCMC package
RDSFiles <- dir(system.file("extdata", package = "RJMCMCNucleosomes"),
full.names = TRUE, pattern = "*RDS")

## Merge nucleosomes info from RDS files present in directory
result <- mergeRDSFiles(RDSFiles)

## Print the number and the position of the nucleosomes
result$k
result$mu

## Class of the output object
class(result)

Generate a graph of nucleosome positions with read coverage

Description

Generate a graph for a GRanges or a GRangesList of nucleosome positions. In presence of only one prediction (with multiples nucleosome positions), a GRanges is used. In presence of more thant one predictions (as example, before and after post-treatment or results from different software), a GRangesList with one entry per prediction is used. All predictions must have been obtained using the same reads.

Usage

plotNucleosomes(nucleosomePositions, reads, seqName = NULL,
  xlab = "position", ylab = "coverage", names = NULL)
plotNucleosomes(nucleosomePositions, reads, seqName = NULL,
  xlab = "position", ylab = "coverage", names = NULL)

Arguments

`nucleosomePositions`	a `GRanges` or a `GRangesList` containing the nucleosome positions for one or multiples predictions obtained using the same reads. In presence of only one prediction (with multiples nucleosome positions), a `GRanges` is used. In presence of more thant one predictions (as example, before and after post-treatment or results from different software), a `GRangesList` with one entry per prediction is used.
`reads`	a `GRanges` containing forward and reverse reads. The `GRanges` should contain at least one read.
`seqName`	a `character` string containing the label of the chromosome, present in the `GRanges` object, that will be used. The `NULL` value is accepted when only one seqname is present in the `GRanges`; the only seqname present will be used. Default: `NULL`.
`xlab`	a `character` string containing the label of the x-axis.
`ylab`	a `character` string containing the label of the y-axis.
`names`	a `vector` of a `character` string containing the label of each prediction set. The `vector` must be the same length of the `nucleosomePositions` `list` or 1 in presence of a `vector`. When `NULL`, the name of the elements of the `list` are used or the string "Nucleosome" for a `vector` are used. Default: `NULL`.

Value

a graph containing the nucleosome positions and the read coverage

Author(s)

Astrid Deschenes

Examples


## Load reads dataset
data(reads_demo_01)

## Run RJMCMC method
result <- rjmcmc(reads = reads_demo_01,
            seqName = "chr_SYNTHETIC",
            nbrIterations = 4000, lambda = 2, kMax = 30,
            minInterval = 146, maxInterval = 292, minReads = 5,
            vSeed = 10213)

## Create graph using the synthetic map
plotNucleosomes(nucleosomePositions = result$mu, seqName = "chr_SYNTHETIC",
            reads = reads_demo_01)

## Load reads dataset
data(reads_demo_01)

## Run RJMCMC method
result <- rjmcmc(reads = reads_demo_01,
            seqName = "chr_SYNTHETIC",
            nbrIterations = 4000, lambda = 2, kMax = 30,
            minInterval = 146, maxInterval = 292, minReads = 5,
            vSeed = 10213)

## Create graph using the synthetic map
plotNucleosomes(nucleosomePositions = result$mu, seqName = "chr_SYNTHETIC",
            reads = reads_demo_01)

A post-treatment function to merge closely positioned nucleosomes, from the same chromosome, identified by the `rjmcmc` function.

Description

A helper function which merges closely positioned nucleosomes to rectify the over splitting and provide a more conservative approach. Beware that each chromosome must be treated separatly.

Usage

postTreatment(reads, seqName = NULL, resultRJMCMC, extendingSize = 74L,
  chrLength)
postTreatment(reads, seqName = NULL, resultRJMCMC, extendingSize = 74L,
  chrLength)

Arguments

`reads`	a `GRanges` containing forward and reverse reads. Beware that the start position of a reverse read is always higher that the end positition.
`seqName`	a `character` string containing the label of the chromosome, present in the `GRanges` object, that will be used. The `NULL` value is accepted when only one seqname is present in the `GRanges`; the only seqname present will be used. Default: `NULL`.
`resultRJMCMC`	an object of `class` "rjmcmcNucleosomes" or "rjmcmcNucleosomesMerge", the information about nucleosome positioning for an entire chromosome or a region that must be treated as one unit.
`extendingSize`	a positive `numeric` or a positive `integer` indicating the size of the consensus region used to group closeley positioned nucleosomes.The minimum size of the consensus region is equal to twice the value of the `extendingSize` parameter. The numeric will be treated as an integer. Default: 74.
`chrLength`	a positive `numeric` or a positive `integer` indicating the length of the current chromosome. The length of the chromosome is used to ensure that the consensus positions are all located inside the chromosome.

Value

a GRanges, the updated nucleosome positions. When no nucleosome is present, NULL is returned.

Author(s)

Pascal Belleau, Astrid Deschenes

Examples


## Loading dataset
data(reads_demo_02)

## Nucleosome positioning, running both merge and split functions
result <- rjmcmc(reads = reads_demo_02,
            seqName = "chr_SYNTHETIC", nbrIterations = 1000,
            lambda = 2, kMax = 30, minInterval = 146,
            maxInterval = 490, minReads = 3, vSeed = 11)

## Before post-treatment
result

##Post-treatment function which merged closely positioned nucleosomes
postResult <- postTreatment(reads = reads_demo_02,
                    seqName = "chr_SYNTHETIC", result, 100, 73500)

## After post-treatment
postResult

## Loading dataset
data(reads_demo_02)

## Nucleosome positioning, running both merge and split functions
result <- rjmcmc(reads = reads_demo_02,
            seqName = "chr_SYNTHETIC", nbrIterations = 1000,
            lambda = 2, kMax = 30, minInterval = 146,
            maxInterval = 490, minReads = 3, vSeed = 11)

## Before post-treatment
result

##Post-treatment function which merged closely positioned nucleosomes
postResult <- postTreatment(reads = reads_demo_02,
                    seqName = "chr_SYNTHETIC", result, 100, 73500)

## After post-treatment
postResult

Formated output of predicted nucleosomes

Description

Generated a formated output of a list marked as an rjmcmcNucleosomes class

Usage

## S3 method for class 'rjmcmcNucleosomes'
print(x, ...)
## S3 method for class 'rjmcmcNucleosomes'
print(x, ...)

Arguments

`x`	the output object from `rjmcmc` function to be printed
`...`	arguments passed to or from other methods

Value

An object of class rjmcmcNucleosomes

Author(s)

Astrid Deschenes

Examples


## Loading dataset
data(RJMCMC_result)

print(RJMCMC_result)

## Loading dataset
data(RJMCMC_result)

print(RJMCMC_result)

Formated output of predicted nucleosomes

Description

Generated a formated output of a list marked as an rjmcmcNucleosomesBeforeAndAfterPostTreatment class

Usage

## S3 method for class 'rjmcmcNucleosomesBeforeAndAfterPostTreatment'
print(x, ...)
## S3 method for class 'rjmcmcNucleosomesBeforeAndAfterPostTreatment'
print(x, ...)

Arguments

`x`	the output object from `rjmcmcCHR` function to be printed
`...`	arguments passed to or from other methods

Value

an object of class rjmcmcNucleosomesBeforeAndAfterPostTreatment

Author(s)

Astrid Deschenes

Examples


## Load synthetic dataset of reads
data(syntheticNucleosomeReads)

## Use dataset of reads to create GRanges object
sampleGRanges <- GRanges(syntheticNucleosomeReads$dataIP)

## Run nucleosome detection on the entire sample
## Not run: result <- rjmcmcCHR(reads = sampleGRanges, zeta = 147, delta=50,
maxLength=1200, nbrIterations = 1000, lambda = 3, kMax = 30,
minInterval = 146, maxInterval = 292, minReads = 5, vSeed = 10113,
nbCores = 2, saveAsRDS = FALSE)
## End(Not run)

## Print result
## Not run: print(result)

## Load synthetic dataset of reads
data(syntheticNucleosomeReads)

## Use dataset of reads to create GRanges object
sampleGRanges <- GRanges(syntheticNucleosomeReads$dataIP)

## Run nucleosome detection on the entire sample
## Not run: result <- rjmcmcCHR(reads = sampleGRanges, zeta = 147, delta=50,
maxLength=1200, nbrIterations = 1000, lambda = 3, kMax = 30,
minInterval = 146, maxInterval = 292, minReads = 5, vSeed = 10113,
nbCores = 2, saveAsRDS = FALSE)
## End(Not run)

## Print result
## Not run: print(result)

Formated output of predicted nucleosomes

Description

Generated a formated output of a list marked as an rjmcmcNucleosomesMerge class

Usage

## S3 method for class 'rjmcmcNucleosomesMerge'
print(x, ...)
## S3 method for class 'rjmcmcNucleosomesMerge'
print(x, ...)

Arguments

`x`	the output object from `mergeAllRDSFilesFromDirectory` function to be printed
`...`	arguments passed to or from other methods

Value

an object of class mergeAllRDSFilesFromDirectory

Author(s)

Astrid Deschenes

Examples


## Use a directory present in the RJMCMC package
directoryWithRDSFiles <- system.file("extdata",
package = "RJMCMCNucleosomes")

## Merge nucleosomes info from RDS files present in directory
## It is assumed that all files present in the directory are nucleosomes
## result for the same chromosome
result <- mergeAllRDSFilesFromDirectory(directoryWithRDSFiles)

## Show resulting nucleosomes
print(result)

## or simply
result

## Use a directory present in the RJMCMC package
directoryWithRDSFiles <- system.file("extdata",
package = "RJMCMCNucleosomes")

## Merge nucleosomes info from RDS files present in directory
## It is assumed that all files present in the directory are nucleosomes
## result for the same chromosome
result <- mergeAllRDSFilesFromDirectory(directoryWithRDSFiles)

## Show resulting nucleosomes
print(result)

## or simply
result

Forward reads and reverse reads in `GRanges` format (for demo purpose).

Description

A group of forward and reverse reads, in a GRanges, that can be used to test the rjmcmc function.

Usage

data(reads_demo_01)
data(reads_demo_01)

Format

A GRanges containing forward and reverse reads.

Value

A GRanges containing forward and reverse reads.

Examples


## Loading dataset
data(reads_demo_01)

## Nucleosome positioning
rjmcmc(reads = reads_demo_01, nbrIterations = 100, lambda = 3, kMax = 30,
            minInterval = 146, maxInterval = 292, minReads = 5)

## Loading dataset
data(reads_demo_01)

## Nucleosome positioning
rjmcmc(reads = reads_demo_01, nbrIterations = 100, lambda = 3, kMax = 30,
            minInterval = 146, maxInterval = 292, minReads = 5)

Forward reads and reverse reads in `GRanges` format (for demo purpose).

Description

A group of forward and reverse reads that can be used to test the rjmcmc function.

Usage

data(reads_demo_02)
data(reads_demo_02)

Format

A GRanges containing forward and reverse reads.

Value

A GRanges containing forward and reverse reads.

Examples


## Loading dataset
data(reads_demo_02)

## Nucleosome positioning
## Since there is only one chromosome present in reads_demo_02, the name
## of the chromosome does not need to be specified
rjmcmc(reads = reads_demo_02, nbrIterations = 150, lambda = 3, kMax = 30,
            minInterval = 144, maxInterval = 290, minReads = 6)

## Loading dataset
data(reads_demo_02)

## Nucleosome positioning
## Since there is only one chromosome present in reads_demo_02, the name
## of the chromosome does not need to be specified
rjmcmc(reads = reads_demo_02, nbrIterations = 150, lambda = 3, kMax = 30,
            minInterval = 144, maxInterval = 290, minReads = 6)

Nucleosome positioning mapping on a segment

Description

Use of a fully Bayesian hierarchical model for chromosome-wide profiling of nucleosome positions based on high-throughput short-read data (MNase-Seq data). Beware that for a genome-wide profiling, each chromosome must be treated separatly. This function is optimized to run on segments that are smaller sections of the chromosome.

Usage

rjmcmc(reads, seqName = NULL, nbrIterations, kMax, lambda = 3, minInterval,
  maxInterval, minReads = 5, adaptIterationsToReads = TRUE, vSeed = -1,
  saveAsRDS = FALSE)
rjmcmc(reads, seqName = NULL, nbrIterations, kMax, lambda = 3, minInterval,
  maxInterval, minReads = 5, adaptIterationsToReads = TRUE, vSeed = -1,
  saveAsRDS = FALSE)

Arguments

`reads`	a `GRanges` containing forward and reverse reads. Beware that the start position of a reverse read is always higher that the end positition.
`seqName`	a `character` string containing the label of the chromosome, present in the `GRanges` object, that will be used. The `NULL` value is accepted when only one seqname is present in the `GRanges`; the only seqname present will be used. Default: `NULL`.
`nbrIterations`	a positive `integer` or `numeric`, the number of iterations. Non-integer values of `nbrIterations` will be casted to `integer` and truncated towards zero.
`kMax`	a positive `integer` or `numeric`, the maximum number of degrees of freedom per region. Non-integer values of `kMax` will be casted to `integer` and truncated towards zero.
`lambda`	a positive `numeric`, the theorical mean of the Poisson distribution. Default: 3.
`minInterval`	a `numeric`, the minimum distance between two nucleosomes.
`maxInterval`	a `numeric`, the maximum distance between two nucleosomes.
`minReads`	a positive `integer` or `numeric`, the minimum number of reads in a potential canditate region. Non-integer values of `minReads` will be casted to `integer` and truncated towards zero. Default: 5.
`adaptIterationsToReads`	a `logical` indicating if the number of iterations must be modified in function of the number of reads. Default: `TRUE`.
`vSeed`	a `integer`. A seed used when reproducible results are needed. When a value inferior or equal to zero is given, a random integer is used. Default: -1.
`saveAsRDS`	a `logical`. When `TRUE`, a RDS file containing the complete output of the c++ rjmcmc() function is created. Default : `FALSE`.

Value

a list of class "rjmcmcNucleosomes" containing:

call the matched call.
k a integer, the final estimation of the number of nucleosomes. 0 when no nucleosome is detected.
mu a GRanges containing the positions of the nucleosomes and '*' as strand. The seqnames of the GRanges correspond to the seqName input value. NA when no nucleosome is detected.
k_max a integer, the maximum number of nucleosomes obtained during the iteration process. NA when no nucleosome is detected.

Author(s)

Rawane Samb, Pascal Belleau, Astrid Deschenes

Examples


## Loading dataset
data(reads_demo_01)

## Nucleosome positioning, running both merge and split functions
result <- rjmcmc(reads = reads_demo_01, seqName = "chr_SYNTHETIC",
            nbrIterations = 1000, lambda = 2, kMax = 30,
            minInterval = 146, maxInterval = 292, minReads = 5,
            vSeed = 10113, saveAsRDS = FALSE)

## Print the final estimation of the number of nucleosomes
result$k

## Print the position of nucleosomes
result$mu

## Print the maximum number of nucleosomes obtained during the iteration
## process
result$k_max

## Loading dataset
data(reads_demo_01)

## Nucleosome positioning, running both merge and split functions
result <- rjmcmc(reads = reads_demo_01, seqName = "chr_SYNTHETIC",
            nbrIterations = 1000, lambda = 2, kMax = 30,
            minInterval = 146, maxInterval = 292, minReads = 5,
            vSeed = 10113, saveAsRDS = FALSE)

## Print the final estimation of the number of nucleosomes
result$k

## Print the position of nucleosomes
result$mu

## Print the maximum number of nucleosomes obtained during the iteration
## process
result$k_max

Nucleosomes obtained by running RJMCMC function using reads from reads_demo_02 dataset (for demo purpose).

Description

A list of class "rjmcmcNucleosomes" which contains the information about the detected nucleosomes.

Usage

data(RJMCMC_result)
data(RJMCMC_result)

Format

A list of class "rjmcmcNucleosomes" containing:

call the matched call.
k a integer, the final estimation of the number of nucleosomes. 0 when no nucleosome is detected.
mu a vector of numeric of length k, the positions of the nucleosomes. NA when no nucleosome is detected.
k_max a integer, the maximum number of nucleosomes obtained during the iteration process. NA when no nucleosome is detected.

Value

A list of class "rjmcmcNucleosomes" containing:

call the matched call.
k a integer, the final estimation of the number of nucleosomes. 0 when no nucleosome is detected.
mu a vector of numeric of length k, the positions of the nucleosomes. NA when no nucleosome is detected.
k_max a integer, the maximum number of nucleosomes obtained during the iteration process. NA when no nucleosome is detected.

Examples


## Loading dataset
data(RJMCMC_result)
data(reads_demo_02)

## Results before post-treatment
RJMCMC_result$mu

## Post-treatment function which merged closely positioned nucleosomes
postResult <- postTreatment(reads = reads_demo_02,
    extendingSize = 60, chrLength = 100000, resultRJMCMC = RJMCMC_result)

## Results after post-treatment
postResult

## Loading dataset
data(RJMCMC_result)
data(reads_demo_02)

## Results before post-treatment
RJMCMC_result$mu

## Post-treatment function which merged closely positioned nucleosomes
postResult <- postTreatment(reads = reads_demo_02,
    extendingSize = 60, chrLength = 100000, resultRJMCMC = RJMCMC_result)

## Results after post-treatment
postResult

Nucleosome positioning mapping on a large segment, up to a chromosome

Description

The function will process by splittingg the GRanges of reads (as example, the reads from a chromosome) in a list of smaller GRanges segments that can be run by the rjmcmc function. All those steps are done automatically.

Usage

rjmcmcCHR(reads, seqName = NULL, zeta = 147, delta, maxLength,
  nbrIterations, kMax, lambda = 3, minInterval, maxInterval, minReads = 5,
  adaptIterationsToReads = TRUE, vSeed = -1, nbCores = 1,
  dirOut = "out", saveAsRDS = FALSE, saveSEG = TRUE)
rjmcmcCHR(reads, seqName = NULL, zeta = 147, delta, maxLength,
  nbrIterations, kMax, lambda = 3, minInterval, maxInterval, minReads = 5,
  adaptIterationsToReads = TRUE, vSeed = -1, nbCores = 1,
  dirOut = "out", saveAsRDS = FALSE, saveSEG = TRUE)

Arguments

`reads`	a `GRanges`, the forward and reverse reads that need to be segmented.
`seqName`	a `character` string containing the label of the chromosome, present in the `GRanges` object, that will be used. The `NULL` value is accepted when only one seqname is present in the `GRanges`; the only seqname present will be used. Default: `NULL`.
`zeta`	a positive `integer` or `numeric`, the length of the nucleosomes. Default: 147.
`delta`	a positive `integer` or `numeric`, the accepted range of overlapping section between segments. The overlapping section being `zeta` + `delta`.
`maxLength`	a positive `integer` or `numeric`, the length of each segment.
`nbrIterations`	a positive `integer` or `numeric`, the number of iterations. Non-integer values of `nbrIterations` will be casted to `integer` and truncated towards zero.
`kMax`	a positive `integer` or `numeric`, the maximum number of degrees of freedom per region. Non-integer values of `kMax` will be casted to `integer` and truncated towards zero.
`lambda`	a positive `numeric`, the theorical mean of the Poisson distribution. Default: 3.
`minInterval`	a `numeric`, the minimum distance between two nucleosomes.
`maxInterval`	a `numeric`, the maximum distance between two nucleosomes.
`minReads`	a positive `integer` or `numeric`, the minimum number of reads in a potential canditate region. Non-integer values of `minReads` will be casted to `integer` and truncated towards zero. Default: 5.
`adaptIterationsToReads`	a `logical` indicating if the number of iterations must be modified in function of the number of reads. Default: `TRUE`.
`vSeed`	a `integer`. A seed used when reproducible results are needed. When a value inferior or equal to zero is given, a random integer is used. Default: -1.
`nbCores`	a positive `integer`, the number of cores used to run in parallel. Default: 1.
`dirOut`	a `character` string. The name of the directory where 2 directories are created (if they don't already exists). The directory "dirOut/results" contents the rjmcmc results for each segment. The directory "dirOut/done" contents file a log file for each segment in RData format. If the log file for a segment is in the directory, the program considers that it is has been processed and run the next segment. Default: "out".
`saveAsRDS`	a `logical`. When `TRUE`, a RDS file containing the complete output of the `rjmcmc` function is created. Default: `FALSE`.
`saveSEG`	a `logical`. When `TRUE`, a RDS file containing the segments generated by `segmentation` function is saved in directory named from paramter `dirOut`. Default: `FALSE`.

Value

a list of class "rjmcmcNucleosomesBeforeAndAfterPostTreatment" containing:

k a integer, the number of nucleosomes.
mu a GRanges containing the positions of the nucleosomes.
kPost a integer, the number of nucleosomes after post-treatment and '*' as strand. The seqnames of the GRanges correspond to the seqName input value. NA when no nucleosome is detected.
muPost a GRanges containing the positions of the nucleosomes after post-treament and '*' as strand. The seqnames of the GRanges correspond to the seqName input value. NA when no nucleosome is detected.

Author(s)

Pascal Belleau, Astrid Deschenes

Examples


## Load synthetic dataset of reads
data(syntheticNucleosomeReads)

## Use dataset of reads to create GRanges object
sampleGRanges <- GRanges(syntheticNucleosomeReads$dataIP)

## Run nucleosome detection on the entire sample
## Not run: result <- rjmcmcCHR(reads = sampleGRanges, zeta = 147, delta=50,
maxLength=1200, nbrIterations = 1000, lambda = 3, kMax = 30,
minInterval = 146, maxInterval = 292, minReads = 5, vSeed = 10113,
nbCores = 2, saveAsRDS = FALSE)
## End(Not run)

## Load synthetic dataset of reads
data(syntheticNucleosomeReads)

## Use dataset of reads to create GRanges object
sampleGRanges <- GRanges(syntheticNucleosomeReads$dataIP)

## Run nucleosome detection on the entire sample
## Not run: result <- rjmcmcCHR(reads = sampleGRanges, zeta = 147, delta=50,
maxLength=1200, nbrIterations = 1000, lambda = 3, kMax = 30,
minInterval = 146, maxInterval = 292, minReads = 5, vSeed = 10113,
nbCores = 2, saveAsRDS = FALSE)
## End(Not run)

Split a `GRanges` containing reads in a list of smaller segments for the `rjmcmc` function.

Description

Split a GRanges of reads (as example, the reads from a chromosome) in a list of smaller GRanges sot that the rjmcmc function can be run on each segments.

Usage

segmentation(reads, zeta = 147, delta, maxLength)
segmentation(reads, zeta = 147, delta, maxLength)

Arguments

`reads`	a `GRanges`, the reads that need to be segmented.
`zeta`	a positive `integer` or `numeric`, the length of the nucleosomes. Default: 147.
`delta`	a positive `integer` or `numeric`, the accepted range of overlapping section between segments. The overlapping section being `zeta` + `delta`.
`maxLength`	a positive `integer` or `numeric`, the length of each segment.

Value

a GRangesList containing all the segments.

Author(s)

Pascal Belleau, Astrid Deschenes

Examples


## Load synthetic dataset of reads
data(syntheticNucleosomeReads)

## Use dataset of reads to create GRanges object
sampleGRanges <- GRanges(seqnames = syntheticNucleosomeReads$dataIP$chr,
    ranges = IRanges(start = syntheticNucleosomeReads$dataIP$start,
    end = syntheticNucleosomeReads$dataIP$end),
    strand = syntheticNucleosomeReads$dataIP$strand)

# Segmentation of the reads
segmentation(reads = sampleGRanges, zeta = 147, delta = 50,
maxLength = 1000)

## Load synthetic dataset of reads
data(syntheticNucleosomeReads)

## Use dataset of reads to create GRanges object
sampleGRanges <- GRanges(seqnames = syntheticNucleosomeReads$dataIP$chr,
    ranges = IRanges(start = syntheticNucleosomeReads$dataIP$start,
    end = syntheticNucleosomeReads$dataIP$end),
    strand = syntheticNucleosomeReads$dataIP$strand)

# Segmentation of the reads
segmentation(reads = sampleGRanges, zeta = 147, delta = 50,
maxLength = 1000)

Simulated dataset of reads generated by `nucleoSim` package (for demo purpose).

Description

A list of class "syntheticNucReads" which contains the information about synthetic reads related to nucleosomes. The datset has been created using a total of 300 well-positioned nucleosomes, 30 fuzzy nucleosomes with variance of reads following a Normal distribution.

Usage

data(syntheticNucleosomeReads)
data(syntheticNucleosomeReads)

Format

A list containing:

call the called that generated the dataset.
dataIP a data.frame with the chromosome name, the starting and ending positions and the direction of all forward and reverse reads for all well-positioned and fuzzy nucleosomes. Paired-end reads are identified with an unique id.
wp a data.frame with the positions of all the well-positioned nucleosomes, as well as the number of paired-reads associated to each one.
fuz a data.frame with the positions of all the fuzzy nucleosomes, as well as the number of paired-reads associated to each one.
paired a data.frame with the starting and ending positions of the reads used to generate the paired-end reads. Paired-end reads are identified with an unique id.

Value

A list containing:

call the called that generated the dataset.
dataIP a data.frame with the chromosome name, the starting and ending positions and the direction of all forward and reverse reads for all well-positioned and fuzzy nucleosomes. Paired-end reads are identified with an unique id.
wp a data.frame with the positions of all the well-positioned nucleosomes, as well as the number of paired-reads associated to each one.
fuz a data.frame with the positions of all the fuzzy nucleosomes, as well as the number of paired-reads associated to each one.
paired a data.frame with the starting and ending positions of the reads used to generate the paired-end reads. Paired-end reads are identified with an unique id.

Package 'RJMCMCNucleosomes'

Help Index

RJMCMCNucleosomes: Bayesian hierarchical model for genome-wide nucleosome positioning with high-throughput short-read data (MNase-Seq)

Description

Author(s)

See Also

Merge nucleosome information from all RDS files present in a same directory. Beware that only nucleosome information from same chromosome should be merged together.

Description

Usage

Arguments

Value

Author(s)

Examples

Merge nucleosome information from selected RDS files.

Description

Usage

Arguments

Value

Author(s)

Examples

Generate a graph of nucleosome positions with read coverage

Description

Usage

Arguments

Value

Author(s)

Examples

A post-treatment function to merge closely positioned nucleosomes, from the same chromosome, identified by the rjmcmc function.

Description

Usage

Arguments

Value

Author(s)

Examples

Formated output of predicted nucleosomes

Description

Usage

Arguments

Value

Author(s)

Examples

Formated output of predicted nucleosomes

Description

Usage

Arguments

Value

Author(s)

Examples

Formated output of predicted nucleosomes

Description

Usage

Arguments

Value

Author(s)

Examples

Forward reads and reverse reads in GRanges format (for demo purpose).

Description

Usage

Format

Value

See Also

Examples

Forward reads and reverse reads in GRanges format (for demo purpose).

Description

Usage

Format

Value

See Also

Examples

Nucleosome positioning mapping on a segment

Description

Usage

Arguments

Value

Author(s)

Examples

Nucleosomes obtained by running RJMCMC function using reads from reads_demo_02 dataset (for demo purpose).

Description

Usage

Format

A post-treatment function to merge closely positioned nucleosomes, from the same chromosome, identified by the `rjmcmc` function.

Forward reads and reverse reads in `GRanges` format (for demo purpose).

Forward reads and reverse reads in `GRanges` format (for demo purpose).

Split a `GRanges` containing reads in a list of smaller segments for the `rjmcmc` function.

Simulated dataset of reads generated by `nucleoSim` package (for demo purpose).