BiocStyle::markdown() library(knitr)

**Package**: `r Rpackage("nucleoSim")`

**Authors**: `r packageDescription("nucleoSim")[["Author"]]`

**Version**: `r packageDescription("nucleoSim")$Version`

**Compiled date**: `r Sys.Date()`

**License**: `r packageDescription("nucleoSim")[["License"]]`

This package and the underlying `r Rpackage("nucleoSim")`

code are
distributed under the Artistic license 2.0. You are free to use and
redistribute this software.

If you use this package for a publication, we would ask you to cite the following:

Samb R, Khadraoui K, Belleau P, et al. (2015). "Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling." Statistical Applications in Genetics and Molecular Biology. Volume 14, Issue 6, Pages 517–532, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, December 2015. doi: 10.1515/sagmb-2014-0098

Flores O et Orozco M (2011). “nucleR: a package for non-parametric nucleosome positioning.” Bioinformatics, 27, pp. 2149–2150. doi: 10.1093/bioinformatics/btr345

`r Rpackage("nucleoSim")`

can simulate datasets for nucleosomes experiments.

The `r Rpackage("nucleoSim")`

package generates synthetic maps with
sequences covering nucleosome regions as well as synthetic maps with
forward and reverse reads (paired-end reads) emulating next-generation
sequencing. Furthermore, synthetic hybridization data of "Tiling Arrays" can
also be generated.

The `r Rpackage("nucleoSim")`

package allows the user to introduce various
'contaminants' into the sequence datasets, such as fuzzy nucleosomes and
missing nucleosomes, in order to be more realistic and to enable the
evaluation of the influence of common 'noise' on the detection of nucleosomes.

- Select the number of well-positioned nucleosomes to generate
- Select the number of well-positioned nucleosomes to randomly delete
- Select the number of fuzzy nucleosomes to add
- Select the type of distribution used to assign the start position to the sequences associated with the nucleosomes
- Select the variance associated with the distribution of the start position of the sequences for the well-positioned nucleosomes (or the start position of the forward reads for paired-end reads)
- Select the variance associated with the distribution of the start position of the sequences for the fuzzy nucleosomes (or the start position of the forward reads for paired-end reads)
- Select the variance associated with the Normal distribution used to assign the length of each sequence (or the distance between start positions of paired-end reads)
- Select the maximum sequence coverage associated with one nucleosome
- Select the length of the DNA linker
- Select the length of the nucleosomes (not recommended changing default value)
- Select a seed when results need to be reproducible

The `r Rpackage("nucleoSim")`

package has been largely inspired by the
*Generating synthetic maps* section of the
Bioconductor `r Biocpkg("nucleR")`

package (Flores et Orozco, 2011).

`r Rpackage("nucleoSim")`

packageAs with any R package, the `r Rpackage("nucleoSim")`

package should first be
loaded with the following command:

```
library(nucleoSim)
```

The packages can generate 2 types of synthetic data sets:

**Synthetic Nucleosome Maps**: A map with complete sequences covering the
nucleosome regions

**Synthetic Nucleosome Samples**: A map with forward and reverse reads
(paired-end reads) emulating those obtained using a next-generation
sequencing technology on a nucleosome map

A synthetic nucleosome map is a section of genome covered by a fixed number
of nucleosomes. Each nucleosome being associated with a specific number of
sequences. The parameters passed to the `syntheticNucMapFromDist()`

function are
going to affect the distribution of the nucleosomes, as well as, the sequences
associated with each nucleosome.

Technically, the synthetic nucleosome map is separated into 3 steps:

**1. Adding well-positioned nucleosomes**

The synthetic nucleosome map is split into a fixed number of
sections (`wp.num`

) of equal length (`(nuc.len + lin.len)`

bases). The center of the nucleosomes is positioned at a fixed number of bases
from the beginning of each section (`round(nuc.len/2)`

bases). Sequences are
assigned, to each nucleosome, using an uniform distribution. The number of
sequences, assigned to each nucleosome, can vary from 1 to `max.cover`

.
The distribution (`distr`

), as
well as the variance (`wp.var`

) are used to add some fluctuation to the
starting position of the sequences, which as a mean position corresponding to
the starting position of a region. Some fluctuation of the length of
the sequence is also added following
a normal distribution with a fixed variance (`len.var`

). The mean
length of the sequences corresponds to the length of the
nucleosomes (`nuc.len`

).

**2. Deleting some well-positioned nucleosomes**

A fixed number of nucleosomes (`wp.del`

) are deleted. Each nucleosome has
an equal probability to be deleted. A
nucleosome is considered deleted when all sequences associated
with it are eliminated.

**3. Adding fuzzy nucleosomes**

A fixed number of fuzzy nucleosomes (`fuz.num`

) are added. The position of the
fuzzy nucleosomes is selected following an uniform distribution. Such as for
the well-positioned nucleosomes, sequences are
assigned, to each fuzzy nucleosome, using an uniform distribution. The number
of sequences assigned can vary from 1 to `max.cover`

.
The distribution (`distr`

), as
well as the variance (`wp.var`

) are used to add some fluctuation to the
starting position of the sequences, which as a mean position corresponding to
the starting position of a region. Some fluctuation of the length of
the sequence is also added following
a normal distribution with a fixed variance (`len.var`

). The mean
length of the sequences corresponds to the length of the
nucleosomes (`nuc.len`

).

This is an example showing how a synthetic nucleosome map can be generated.

wp.num <- 20 ### Number of well-positioned nucleosomes wp.del <- 5 ### Number of well-positioned nucleosomes to delete wp.var <- 30 ### variance associated with the starting ### position of the sequences of the ### well-positioned nucleosomes fuz.num <- 5 ### Number of fuzzy nucleosomes fuz.var <- 50 ### Variance associated with the starting ### positions of the sequences for the ### fuzzy nucleosomes max.cover <- 70 ### Maximum sequences associated with one ### nucleosome (default: 100) nuc.len <- 147 ### Length of the nucleosome (default: 147) len.var <- 12 ### variance associated with the length of ### the sequences (default: 10) lin.len <- 20 ### Length of the DNA linker (default: 20) distr <- "Normal" ### Type of distribution to use rnd.seed <- 210001 ### Set seed when result needs to be reproducible #### Create a synthetic nucleosome map nucleosomeMap <- syntheticNucMapFromDist(wp.num=wp.num, wp.del=wp.del, wp.var=wp.var, fuz.num=fuz.num, fuz.var=fuz.var, max.cover=max.cover, nuc.len=nuc.len, len.var=len.var, lin.len=lin.len, rnd.seed=rnd.seed, distr=distr) #### The start positions of all well-positioned nucleosomes nucleosomeMap$wp.starts #### The number of sequences associated with each well-positioned nucleosome nucleosomeMap$wp.nreads #### IRanges object containing all sequences for the well-positioned nucleosomes head(nucleosomeMap$wp.reads, n = 2) #### The start positions of all fuzzy nucleosomes nucleosomeMap$fuz.starts #### The number of sequences associated with each fuzzy nucleosome nucleosomeMap$fuz.nreads #### A IRanges object containing all sequences for the fuzzy nucleosomes head(nucleosomeMap$fuz.reads, n = 2) #### A IRanges object containing all sequences for all nucleosomes head(nucleosomeMap$syn.reads, n = 2)

The synthetic nucleosome map can easily be visualized using `plot()`

function.
On the graph, each nucleosome is located on the graph using the
coordonnates:

```
(x,y) =
(the central position of the nucleosome, the number of sequences
associated with the nucleosome)
```

#### Create visual representation of the synthetic nucleosome map plot(nucleosomeMap, xlab="Position", ylab="Coverage")

The `syntheticNucMapFromDist()`

function contains an option (`as.ratio`

) which
enable the simulation of hybridization data of "*Tiling Arrays*". The data are
generated by calculating the ratio between the nucleosome map and
a control map of random sequences created using a uniform distribution. The
control map simulates a DNA randomly fragmented sample.

This is an example showing how a synthetic nucleosome map can be generated.

as.ratio <- TRUE ### Activate the simulation of hybridization data rnd.seed <- 212309 ### Set seed when result needs to be reproducible #### Create a synthetic nucleosome map with hybridization data nucleosomeMapTiling <- syntheticNucMapFromDist(wp.num=10, wp.del=2, wp.var=20, fuz.num=1, fuz.var=32, max.cover=50, nuc.len=145, len.var=3, lin.len=40, rnd.seed=rnd.seed, as.ratio=as.ratio, distr="Uniform") #### Control sequences for hybridization data (only when as.ratio = TRUE) head(nucleosomeMapTiling$ctr.reads, n=4) #### Ratio for hybridization data (only when as.ratio = TRUE) head(nucleosomeMapTiling$syn.ratio, n=4) #### Create visual representation of the synthetic nucleosome map plot(nucleosomeMapTiling)

A synthetic nucleosome sample is a map with forward and reverse reads (paired-end reads) emulating those obtained using a next-generation sequencing technology. It is created using the same first 3 steps than the synthetic nucleosome map. However, some new steps are present:

**1. Adding well-positioned nucleosomes **

The synthetic nucleosome map is split into a fixed number of
sections (`wp.num`

) of equal length (`(nuc.len + lin.len)`

bases). The center of the nucleosomes are positioned at a fixed number of
bases from the beginning of each section (`round(nuc.len/2)`

bases).
Paired-end reads are assigned, to each nucleosome, using an uniform
distribution. The number of paired-end reads, assigned to each
nucleosome, can vary from 1 to `max.cover`

. The distribution (`distr`

), as
well as the variance (`wp.var`

) are used to add some fluctuation to the
starting position of the forward reads, which as a mean position corresponding
to the starting position of a region. Some fluctuation of the distance between
start positions of paired-end reads is added following
a normal distribution with a fixed variance (`len.var`

). The mean
distance between start positions of paired-end reads corresponds to
the length of the nucleosomes (`nuc.len`

).

**2. Deleting some well-positioned nucleosomes**

A fixed number of nucleosomes (`wp.del`

) are deleted. Each nucleosome has
an equal probability to be deleted. A nucleosome is considered deleted
when all paired-end reads
associated with it are eliminated.

**3. Adding fuzzy nucleosomes**

A fixed number of fuzzy nucleosomes (`fuz.num`

) are added. The position of the
fuzzy nucleosomes is selected following an uniform distribution. Such as for
the well-positioned nucleosomes, reads are
assigned, to each fuzzy nucleosome, using an uniform distribution. The number
of paired-end reads assigned can vary from 1 to `max.cover`

.
The distribution (`distr`

), as
well as the variance (`wp.var`

) are used to add some fluctuation to the
starting position of the forward reads, which as a mean position corresponding
to the starting position of a region. Some fluctuation of the distance between
start positions of paired-end reads is also added following
a normal distribution with a fixed variance (`len.var`

). The mean distance
between start positions of paired-end reads corresponds to the length of
the nucleosomes (`nuc.len`

). All reads have a fixed length
(`read.len`

).

**4. Adding an offset**

An offset (`offset`

) is added to all nucleosomes and
reads positions to ensure that all values are positive (mainly pertinent for
reads).

This function needs information about the nucleosomes and their distribution
to generate a nucleosome sample. The output is of class `syntheticNucMap`

.

wp.num <- 30 ### Number of well-positioned nucleosomes wp.del <- 10 ### Number of well-positioned nucleosomes ### to delete wp.var <- 30 ### variance associated with the starting ### positions of the sequences for the ### well-positioned nucleosomes fuz.num <- 10 ### Number of fuzzy nucleosomes fuz.var <- 50 ### Variance associated with the starting ### positions of the sequences for the ### fuzzy nucleosomes max.cover <- 90 ### Maximum paired-end reads associated with ### one nucleosome (default: 100) nuc.len <- 147 ### Length of the nucleosome (default: 147) len.var <- 12 ### variance associated with the distance ### between start positions of ### paired-end reads (default: 10) lin.len <- 20 ### Length of the DNA linker (default: 20) read.len <- 45 ### Length of the generated forward and ### reverse reads (default: 40) distr <- "Uniform" ### Type of distribution to use offset <- 10000 ### The number of bases used to offset ### all nucleosomes and reads rnd.seed <- 202 ### Set seed when result needs to be ### reproducible #### Create Uniform sample nucleosomeSample <- syntheticNucReadsFromDist(wp.num=wp.num, wp.del=wp.del, wp.var=wp.var, fuz.num=fuz.num, fuz.var=fuz.var, max.cover=max.cover, nuc.len=nuc.len, len.var=len.var, read.len=read.len, lin.len=lin.len, rnd.seed=rnd.seed, distr=distr, offset=offset) #### The central position of all well-positioned nucleosomes with the #### number of paired-end reads each associated with each one head(nucleosomeSample$wp, n = 2) #### The central position of all fuzzy nucleosomes with the #### number of paired-end reads each associated with each one head(nucleosomeSample$fuz, n = 2) #### A data.frame with the name of the synthetic chromosome, the starting #### position, the ending position and the direction of all forward and #### reverse reads head(nucleosomeSample$dataIP, n = 2)

The synthetic nucleosome sample can easily be visualized using `plot()`

function. On the graph, each nucleosome is located on the graph using the
coordinates:

```
(x,y) =
(the central position of the nucleosome, the number of paired-end reads associated with
the nucleosome)
```

#### Create visual representation of the synthetic nucleosome sample plot(nucleosomeSample, xlab="Position", ylab="Coverage (number of reads)")

A synthetic nucleosome sample can be created using a nucleosome map. The
nucleosomes and reads present in the nucleosome map will be added an offset.
Forward and reverse reads will also be generated. The output is of class
`syntheticNucMap`

.

#### A nucleosome map has already been created class(nucleosomeMap) #### read.len <- 45 ### The length of the reverse and forward reads offset <- 500 ### The number of bases used to offset all nucleosomes and reads #### Create nucleosome sample nucleosomeSampleFromMap <- syntheticNucReadsFromMap(nucleosomeMap, read.len=read.len, offset=offset) #### A data.frame with the name of the synthetic chromosome, the starting #### position, the ending position and the direction of all forward and #### reverse reads head(nucleosomeSampleFromMap$dataIP, n = 2)

Here is the output of `sessionInfo()`

on the system on which this document was
compiled:

```
sessionInfo()
```

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