LBMC » NucleoMiner

References

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Python Reference

================

configurator.CSV_SAMPLE_FILE = None

   Path to cvs file that contains sample information.

configurator.BOWTIE_BUILD_BIN = None

   Path for bowtie2 build bin.

configurator.BOWTIE2_BIN = None

   Path for bowtie2 bin.

configurator.SAMTOOLS_BIN = None

   Path for samtools bin.

configurator.BEDTOOLS_BIN = None

   Path for bedtools bin.

configurator.TF_BIN = None

   Path for TemplateFilter bin.

configurator.TF_TEMPLATES_FILE = None

   Path for TemplateFilter templates file.

configurator.ILLUMINA_OUTPUTFILE_PREFIX = None

   Prefix for Illumina fastq output files.

configurator.INDEX_DIR = None

   Path for index dir.

configurator.ALIGN_DIR = None

   Path for align dir.

configurator.LOG_DIR = None

   Path for log dir

configurator.CACHE_DIR = None

   Path for cache dir.

configurator.RESULTS_DIR = None

   Path for results dir

configurator.FASTA_REFERENCE_GENOME_FILES = None

   Dictionary where each fasta reference genomes is indexed by

   reference strain that it corresponds.

configurator.AREA_BLACK_LIST = None

   Dictionary where keys are strain and values are black listed of

   geneome region.

configurator.FASTA_INDEXES = None

   Dictionary of strain that indexes dictionaries where keys are

   chromosome reference from Fastq file and value are its

   correspondance for Templatefilter.

configurator.C2C_FILES = None

   Dictionary where each strain combination indexes genome aligment.

configurator.READ_LENGTH = None

   Length of Illumina reads.

configurator.MAPQ_THRES = None

   Aligment quality thresold.

configurator.TF_CORR = None

   TemplateFilter Template correlation threshold.

configurator.TF_MINW = None

   TemplateFilter minimum width of a nucleosome.

configurator.TF_MAXW = None

   TemplateFilter maximum  width of a nucleosome.

configurator.TF_OL = None

   TemplateFilter maximum allowed overlap for two nucleosomes.

wf.json_conf_file = 'src/current/nucleominer_config.json'

   Path to the json configuration file.

wf.samples = []

   List of samples where a sample is identify by an id (key: *id*) and

   a strain name (key *strain*).

wf.samples_mnase = []

   List of Mnase samples.

wf.strains = []

   List of reference strains.

libcoverage.create_bowtie_index(strain, strain_fasta_ref, index_dir, bowtie_build_bin)

   Creates bowtie index for a strain *strain*.

   Parameters:

      * **strain** -- the strain reference.

      * **strain_fasta_ref** -- fasta reference genome.

      * **index_dir** -- directories where to put bowtie index.

      * **bowtie_build_bin** -- bowtie2 build binary.

libcoverage.align_reads(sample, align_dir, log_dir, index_dir, illumina_outputfile_prefix, bowtie2_bin, samtools_bin, bedtools_bin)

   Aligns reads to reference genomes. It produces .sam files, that are

   converted to .bam, that are converted to .bed.

   Parameters:

      * **sample** -- a dict that describe a sample.

      * **align_dir** -- directory where aligned reads will be

        stored.

      * **log_dir** -- directory where logs will be stored.

      * **illumina_outputfile_prefix** -- prefix of Illumina

        sequencer fastq.gz output files.

      * **bowtie2_bin** -- bowtie2 binary.

      * **samtools_bin** -- samtools binary.

      * **bedtools_bin** -- bedtools binary.

      * **index_dir** -- bowtie index directory.

libcoverage.split_fr_4_TF(sample, align_dir, fasta_indexes, area_black_list, read_length, mapq_thres)

   Create TempleFilter input files form bed files. This function

   appends in two times. First, it collects reads from bed files and

   feeds a datastructure

   Parameters:

      * **sample** -- a dict that describe a sample.

      * **align_dir** -- directory where aligned reads will be

        stored.

      * **fasta_index** -- the chr reference from the illumina

        output file.

      * **area_black_list** -- the description of genome that will

        be omit.

      * **read_length** -- Length of Illumina reads.

      * **mapq_thres** -- mapping quality criterion threshold, see

        MAPQ in BED/BAM file format.

libcoverage.template_filter(sample, align_dir, log_dir, tf_bin, tf_templates_file, corr, minw, maxw, ol)

   Run TemplateFilter on a specifi sample. It produces .tab file.

   Parameters:

      * **sample** -- a dict that describe a sample.

      * **align_dir** -- directory where aligned reads will be

        stored.

      * **log_dir** -- directory where logs will be stored.

      * **tf_bin** -- path to the TemplateFilter binary.

      * **tf_templates_file** -- path to the TemplateFilter

        templates file.

      * **corr** -- correlation threshold transmits to

        TemplateFilter.

      * **minw** -- minimum width of a nuc, transmits to

        TemplateFilter.

      * **maxw** -- maximum width of a nuc, transmits to

        TemplateFilter.

      * **ol** -- maximum overlaps for 2 nuc, transmits to

        TemplateFilter.

R Reference

===========

Arabic to Roman pair list.

--------------------------

Description

~~~~~~~~~~~

Util to convert Arabicto Roman

Usage

~~~~~

   ARAB2ROM()

Author(s)

~~~~~~~~~

Florent Chuffart

R: False Discovery Rate

False Discovery Rate

--------------------

Description

~~~~~~~~~~~

From a vector x of independent p-values, extract the cutoff

corresponding to the specified FDR. See Benjamini & Hochberg 1995

paper

Usage

~~~~~

   FDR(x, FDR)

Arguments

~~~~~~~~~

"x"

A vector x of independent p-values.

"FDR"

The specified FDR.

Value

~~~~~

Return the the corresponding cutoff.

Author(s)

~~~~~~~~~

Gael Yvert, Florent Chuffart

Examples

~~~~~~~~

   print("example")

R: Roman to Arabic pair list.

Roman to Arabic pair list.

--------------------------

Description

~~~~~~~~~~~

Util to convert Roman to Arabic

Usage

~~~~~

   ROM2ARAB()

Author(s)

~~~~~~~~~

Florent Chuffart

R: Aggregate replicated sample's nucleosomes.

Aggregate replicated sample's nucleosomes.

------------------------------------------

Description

~~~~~~~~~~~

This function aggregates nucleosome for replicated samples. It uses

TemplateFilter ouput of each sample as replicate. Each sample owns a

set of nucleosomes computed using TemplateFilter and ordered by the

position of their center. Adajacent nucleosomes are compared two by

two. Comparison is based on a log likelihood ratio score. The issue of

comparison is adjacents nucleosomes merge or separation. Finally the

function returns a list of clusters and all computed *llr_scores*.

Each cluster ows an attribute *wp* for "well positionned". This

attribute is set as *TRUE* if the cluster is composed of exactly one

nucleosomes of each sample.

Usage

~~~~~

   aggregate_intra_strain_nucs(samples, llr_thres = 20, coord_max = 2e+07)

Arguments

~~~~~~~~~

"samples"

A list of samples. Each sample is a list like *sample = list(id=...,

marker=..., strain=..., roi=..., inputs=..., outputs=...)* with *roi =

list(name=..., begin=..., end=..., chr=..., genome=...)*.

"llr_thres"

Log likelihood ration threshold.

"coord_max"

A too big value to be a coord for a nucleosome lower bound.

Value

~~~~~

Returns a list of clusterized nucleosomes, and all computed llr

scores.

Author(s)

~~~~~~~~~

Florent Chuffart

Examples

~~~~~~~~

   # Dealing with a region of interest

   roi =list(name="example", begin=1000,  end=1300, chr="1", genome=rep("A",301))

   samples = list()

   for (i in 1:3) {

       # Create TF output

       tf_nuc = list("chr"=paste("chr", roi$chr, sep=""), "center"=(roi$end + roi$begin)/2, "width"= 150, "correlation.score"= 0.9)

       outputs = dfadd(NULL,tf_nuc)

       outputs = filter_tf_outputs(outputs, roi$chr, roi$begin, roi$end)

       # Generate corresponding reads

       nb_reads = round(runif(1,170,230))

       reads = round(rnorm(nb_reads, tf_nuc$center,20))

       u_reads = sort(unique(reads))

       strands = sample(c(rep("R",ceiling(length(u_reads)/2)),rep("F",floor(length(u_reads)/2))))

       counts = apply(t(u_reads), 2, function(r) { sum(reads == r)})

       shifts = apply(t(strands), 2, function(s) { if (s == "F") return(-tf_nuc$width/2) else return(tf_nuc$width/2)})

       u_reads = u_reads + shifts

       inputs = data.frame(list("V1" = rep(roi$chr, length(u_reads)),

                                "V2" = u_reads,

                                                        "V3" = strands,

                                                        "V4" = counts), stringsAsFactors=FALSE)

       samples[[length(samples) + 1]] = list(id=1, marker="Mnase_Seq", strain="strain_ex", total_reads = 10000000, roi=roi, inputs=inputs, outputs=outputs)

   }

   print(aggregate_intra_strain_nucs(samples))

R: Aligns nucleosomes between 2 strains.

Aligns nucleosomes between 2 strains.

-------------------------------------

Description

~~~~~~~~~~~

This function aligns nucs between two strains for a given genome

region.

Usage

~~~~~

   align_inter_strain_nucs(replicates, wp_nucs_strain_ref1 = NULL,

       wp_nucs_strain_ref2 = NULL, corr_thres = 0.5, llr_thres = 100,

       config = NULL, ...)

Arguments

~~~~~~~~~

"replicates"

Set of replicates, ideally 3 per strain.

"wp_nucs_strain_ref1"

List of aggregates nucleosome for strain 1. If it's null this list

will be computed.

"wp_nucs_strain_ref2"

List of aggregates nucleosome for strain 2. If it's null this list

will be computed.

"corr_thres"

Correlation threshold.

"llr_thres"

LOD cut off.

"config"

GLOBAL config variable

"..."

A list of parameters that will be passed to

*aggregate_intra_strain_nucs* if needed.

Value

~~~~~

Returns a list of clusterized nucleosomes, and all computed llr

scores.

Author(s)

~~~~~~~~~

Florent Chuffart

Examples

~~~~~~~~

       # Define new translate_cur function...

       translate_cur = function(roi, strain2, big_cur=NULL, config=NULL) {

         return(roi)

       }

       # Binding it by uncomment follwing lines.

       unlockBinding("translate_cur", as.environment("package:nucleominer"))

       unlockBinding("translate_cur", getNamespace("nucleominer"))

       assign("translate_cur", translate_cur, "package:nucleominer")

       assign("translate_cur", translate_cur, getNamespace("nucleominer"))

       lockBinding("translate_cur", getNamespace("nucleominer"))

       lockBinding("translate_cur", as.environment("package:nucleominer"))

   # Dealing with a region of interest

   roi =list(name="example", begin=1000,  end=1300, chr="1", genome=rep("A",301), strain_ref1 = "STRAINREF1")

   roi2 = translate_cur(roi, roi$strain_ref1)

   replicates = list()

   for (j in 1:2) {

       samples = list()

       for (i in 1:3) {

           # Create TF output

           tf_nuc = list("chr"=paste("chr", roi$chr, sep=""), "center"=(roi$end + roi$begin)/2, "width"= 150, "correlation.score"= 0.9)

           outputs = dfadd(NULL,tf_nuc)

           outputs = filter_tf_outputs(outputs, roi$chr, roi$begin, roi$end)

           # Generate corresponding reads

           nb_reads = round(runif(1,170,230))

           reads = round(rnorm(nb_reads, tf_nuc$center,20))

           u_reads = sort(unique(reads))

           strands = sample(c(rep("R",ceiling(length(u_reads)/2)),rep("F",floor(length(u_reads)/2))))

           counts = apply(t(u_reads), 2, function(r) { sum(reads == r)})

           shifts = apply(t(strands), 2, function(s) { if (s == "F") return(-tf_nuc$width/2) else return(tf_nuc$width/2)})

           u_reads = u_reads + shifts

           inputs = data.frame(list("V1" = rep(roi$chr, length(u_reads)),

                                    "V2" = u_reads,

                                                            "V3" = strands,

                                                            "V4" = counts), stringsAsFactors=FALSE)

           samples[[length(samples) + 1]] = list(id=1, marker="Mnase_Seq", strain=paste("strain_ex",j,sep=""), total_reads = 10000000, roi=roi, inputs=inputs, outputs=outputs)

       }

       replicates[[length(replicates) + 1]] = samples

   }

   print(align_inter_strain_nucs(replicates))

R: Launch deseq methods.

Launch deseq methods.

---------------------

Description

~~~~~~~~~~~

This function is based on deseq example. It mormalizes data, fit data

to GLM model with and without interaction term and compare the two

l;=models.

Usage

~~~~~

   analyse_design(snep_design, reads)

Arguments

~~~~~~~~~

"snep_design"

The design to considere.

"reads"

The data to considere.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Stage replicates data

Stage replicates data

---------------------

Description

~~~~~~~~~~~

This function loads in memory data corresponding to the given

experiments.

Usage

~~~~~

   build_replicates(expe, roi, only_fetch = FALSE, get_genome = FALSE,

       all_samples, config = NULL)

Arguments

~~~~~~~~~

"expe"

a list of vector corresponding to vector of replicates.

"roi"

the region that we are interested in.

"only_fetch"

filter or not inputs.

"get_genome"

Load or not corresponding genome.

"all_samples"

Global list of samples.

"config"

GLOBAL config variable.

Author(s)

~~~~~~~~~

Florent Chuffart

Examples

~~~~~~~~

   # library(rjson)

   # library(nucleominer)

   #

   # # Read config file

   # json_conf_file = "nucleo_miner_config.json"

   # config = fromJSON(paste(readLines(json_conf_file), collapse=""))

   # # Read sample file

   # all_samples = get_content(config$CSV_SAMPLE_FILE, "cvs", sep=";", head=TRUE, stringsAsFactors=FALSE)

   # # here are the sample ids in a list

   # expes = list(c(1))

   # # here is the region that we wnt to see the coverage

   # cur = list(chr="8", begin=472000, end=474000, strain_ref="BY")

   # # it displays the corverage

   # replicates = build_replicates(expes, cur, all_samples=all_samples, config=config)

   # out = watch_samples(replicates, config$READ_LENGTH,

   #       plot_coverage = TRUE,

   #       plot_squared_reads = FALSE,

   #       plot_ref_genome = FALSE,

   #       plot_arrow_raw_reads = FALSE,

   #       plot_arrow_nuc_reads = FALSE,

   #       plot_gaussian_reads = FALSE,

   #       plot_gaussian_unified_reads = FALSE,

   #       plot_ellipse_nucs = FALSE,

   #       plot_wp_nucs = FALSE,

   #       plot_wp_nuc_model = FALSE,

   #       plot_common_nucs = FALSE,

   #       height = 50)

R: Extract a sub part of the corresponding c2c file

Extract a sub part of the corresponding c2c file

------------------------------------------------

Description

~~~~~~~~~~~

This fonction allow to acces to a specific part of the c2c file.

Usage

~~~~~

   c2c_extraction(strain1, strain2, chr = NULL, lower_bound = NULL,

       upper_bound = NULL, config = NULL)

Arguments

~~~~~~~~~

"strain1"

the key strain

"strain2"

the target strain

"chr"

if defined, the c2c will filtered according to the chromosome value

"lower_bound"

if defined, the c2c will filtered for part of the genome upper than

lower_bound

"upper_bound"

if defined, the c2c will filtered for part of the genome lower than

upper_bound

"config"

GLOBAL config variable

Author(s)

~~~~~~~~~

Florent Chuffart

R: reformat an "apply manipulated" list of regions

reformat an "apply manipulated" list of regions

-----------------------------------------------

Description

~~~~~~~~~~~

Utils to reformat an "apply manipulated" list of regions

Usage

~~~~~

   collapse_regions(regions)

Arguments

~~~~~~~~~

+-----------------+------+

+-----------------+------+

Author(s)

~~~~~~~~~

Florent Chuffart

R: Compute Common Uninterrupted Regions (CUR)

Compute Common Uninterrupted Regions (CUR)

------------------------------------------

Description

~~~~~~~~~~~

CURs are regions that can be aligned between the genomes

Usage

~~~~~

   compute_inter_all_strain_curs(diff_allowed = 30, min_cur_width = 4000,

       config = NULL)

Arguments

~~~~~~~~~

"diff_allowed"

the maximum indel width allowe din a CUR

"min_cur_width"

The minimum width of a CUR

"config"

GLOBAL config variable

Author(s)

~~~~~~~~~

Florent Chuffart

R: Crop bound of regions according to region of interest bound

Crop bound of regions according to region of interest bound

-----------------------------------------------------------

Description

~~~~~~~~~~~

The fucntion is no more necessary since we remove "big_cur" bug in

translate_cur function.

Usage

~~~~~

   crop_fuzzy(tmp_fuzzy_nucs, roi, strain, config = NULL)

Arguments

~~~~~~~~~

"tmp_fuzzy_nucs"

the regiuons to be croped.

"roi"

The region of interest.

"strain"

The strain to consider.

"config"

GLOBAL config variable

Author(s)

~~~~~~~~~

Florent Chuffart

R: Adding list to a dataframe.

Adding list to a dataframe.

---------------------------

Description

~~~~~~~~~~~

Add a list *l* to a dataframe *df*. Create it if *df* is *NULL*.

Return the dataframe *df*.

Usage

~~~~~

   dfadd(df, l)

Arguments

~~~~~~~~~

"df"

A dataframe

"l"

A list

Value

~~~~~

Return the dataframe *df*.

Author(s)

~~~~~~~~~

Florent Chuffart

Examples

~~~~~~~~

   ## Here dataframe is NULL

   print(df)

   df = NULL

   # Initialize df

   df = dfadd(df, list(key1 = "value1", key2 = "value2"))

   print(df)

   # Adding elements to df

   df = dfadd(df, list(key1 = "value1'", key2 = "value2'"))

   print(df)

R: Prefetch data

Prefetch data

-------------

Description

~~~~~~~~~~~

Fetch and filter inputs and outpouts per region of interest. Organize

it per replicates.

Usage

~~~~~

   fetch_mnase_replicates(strain, roi, all_samples, config = NULL,

       only_fetch = FALSE, get_genome = FALSE, get_ouputs = TRUE)

Arguments

~~~~~~~~~

"strain"

The strain we want mnase replicatesList of replicates. Each replicates

is a vector of sample ids.

"roi"

Region of interest.

"all_samples"

Global list of samples.

"config"

GLOBAL config variable

"only_fetch"

If TRUE, only fetch and not filtering. It is used tio load sample

files into memory before forking.

"get_genome"

If TRUE, load corresponding genome sequence.

"get_ouputs"

If TRUE, get also ouput corresponding TF output files.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Filter TemplateFilter inputs

Filter TemplateFilter inputs

----------------------------

Description

~~~~~~~~~~~

This function filters TemplateFilter inputs according genome area

observed properties. It takes into account reads that are at the

frontier of this area and the strand of these reads.

Usage

~~~~~

   filter_tf_inputs(inputs, chr, x_min, x_max, nuc_width = 160,

       only_f = FALSE, only_r = FALSE, filter_for_coverage = FALSE)

Arguments

~~~~~~~~~

"inputs"

TF inputs to be filtered.

"chr"

Chromosome observed, here chr is an integer.

"x_min"

Coordinate of the first bp observed.

"x_max"

Coordinate of the last bp observed.

"nuc_width"

Nucleosome width.

"only_f"

Filter only F reads.

"only_r"

Filter only R reads.

"filter_for_coverage"

Does it filter for plot coverage?

Value

~~~~~

Returns filtred inputs.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Filter TemplateFilter outputs

Filter TemplateFilter outputs

-----------------------------

Description

~~~~~~~~~~~

This function filters TemplateFilter outputs according, not only

genome area observerved properties, but also correlation and

overlapping threshold.

Usage

~~~~~

   filter_tf_outputs(tf_outputs, chr, x_min, x_max, nuc_width = 160,

       ol_bp = 59, corr_thres = 0.5)

Arguments

~~~~~~~~~

"tf_outputs"

TemplateFilter outputs.

"chr"

Chromosome observed, here chr is an integer.

"x_min"

Coordinate of the first bp observed.

"x_max"

Coordinate of the last bp observed.

"nuc_width"

Nucleosome width.

"ol_bp"

Overlap Threshold.

"corr_thres"

Correlation threshold.

Value

~~~~~

Returns filtered TemplateFilter Outputs

Author(s)

~~~~~~~~~

Florent Chuffart

R: to flat aggregate_intra_strain_nucs function output

to flat aggregate_intra_strain_nucs function output

---------------------------------------------------

Description

~~~~~~~~~~~

This function builds a dataframe of all clusters obtain from

aggregate_intra_strain_nucs function.

Usage

~~~~~

   flat_aggregated_intra_strain_nucs(partial_strain_maps, cur_index)

Arguments

~~~~~~~~~

"partial_strain_maps"

the output of aggregate_intra_strain_nucs function

"cur_index"

the index of the roi involved

Value

~~~~~

Returns a dataframe of all clusters obtain from

aggregate_intra_strain_nucs function.

Author(s)

~~~~~~~~~

Florent Chuffart

R: flat reads

flat reads

----------

Description

~~~~~~~~~~~

Extract reads coordinates from TempleteFilter input sequence

Usage

~~~~~

   flat_reads(reads, nuc_width)

Arguments

~~~~~~~~~

"reads"

TemplateFilter input reads

"nuc_width"

Width used to shift F and R reads.

Value

~~~~~

Returns a list of F reads, R reads and joint/shifted F and R reads.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Retrieve Reads

Retrieve Reads

--------------

Description

~~~~~~~~~~~

Retrieve reads for a given marker, combi, form.

Usage

~~~~~

   get_all_reads(marker, combi, form = "wp", config = NULL)

Arguments

~~~~~~~~~

"marker"

The marker to considere.

"combi"

The starin combination to considere.

"form"

The nuc form to considere.

"config"

GLOBAL config variable

Author(s)

~~~~~~~~~

Florent Chuffart

R: get comp strand

get comp strand

---------------

Description

~~~~~~~~~~~

Compute the complementatry strand.

Usage

~~~~~

   get_comp_strand(strand)

Arguments

~~~~~~~~~

"strand"

The original strand.

Value

~~~~~

Returns the complementatry strand.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Build the design for deseq

Build the design for deseq

--------------------------

Description

~~~~~~~~~~~

This function build the design according sample properties.

Usage

~~~~~

   get_design(marker, combi, all_samples)

Arguments

~~~~~~~~~

"marker"

The marker to considere.

"combi"

The starin combination to considere.

"all_samples"

Global list of samples.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Compute the fuzzy list for a given strain.

Compute the fuzzy list for a given strain.

------------------------------------------

Description

~~~~~~~~~~~

This function grabs the nucleosomes detxted by template_filter that

have been rejected bt aggregate_intra_strain_nucs as well positions.

Usage

~~~~~

   get_intra_strain_fuzzy(wp_map, roi, strain, config = NULL)

Arguments

~~~~~~~~~

"wp_map"

Well positionned nucleosomes map.

"roi"

The region of interest.

"strain"

The strain we want to extracvt the fuzzy map.

"config"

GLOBAL config variable.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Compute the list of SNEPs for a given set of marker, strain...

Compute the list of SNEPs for a given set of marker, strain combination and nuc form.

-------------------------------------------------------------------------------------

Description

~~~~~~~~~~~

This function uses

Usage

~~~~~

   get_sneps(marker, combi, form, all_samples, config = NULL)

Arguments

~~~~~~~~~

"marker"

The marker involved.

"combi"

The strain combination involved.

"form"

the nuc form involved.

"all_samples"

Global list of samples.

"config"

GLOBAL config variable

Author(s)

~~~~~~~~~

Florent Chuffart

Examples

~~~~~~~~

   marker = "H3K4me1"

   combi = c("BY", "YJM")

   form = "wpunr" # "wp" | "unr" | "wpunr"

   # foo = get_sneps(marker, combi, form)

   # foo = get_sneps("H4K12ac", c("BY", "RM"), "wp")

R: Compute the unaligned nucleosomal regions (UNRs).

Compute the unaligned nucleosomal regions (UNRs).

-------------------------------------------------

Description

~~~~~~~~~~~

This function aggregate non common wp nucs for each strain and

substract common wp nucs. It does not take care about the size of the

resulting UNR. It will be take into account in the count read part og

the pipeline.

Usage

~~~~~

   get_unrs(combi, roi, cur_index, wp_maps, fuzzy_maps, common_nuc_results,

       config = NULL)

Arguments

~~~~~~~~~

"combi"

The strain combination to consider.

"roi"

The region of interest.

"cur_index"

The region of interest index.

"wp_maps"

Well positionned nucleosomes maps.

"fuzzy_maps"

Fuzzy nucleosomes maps.

"common_nuc_results"

Common wp nuc maps

"config"

GLOBAL config variable

Author(s)

~~~~~~~~~

Florent Chuffart

R: Returns the intersection of 2 list on regions.

Returns the intersection of 2 list on regions.

----------------------------------------------

Description

~~~~~~~~~~~

This function...

Usage

~~~~~

   intersect_region(region1, region2)

Arguments

~~~~~~~~~

"region1"

Original regions.

"region2"

Regions to intersect.

Author(s)

~~~~~~~~~

Florent Chuffart

R: Likelihood ratio

Likelihood ratio

----------------

Description

~~~~~~~~~~~

Compute the log likelihood ratio of two or more set of value.

Usage

~~~~~

   llr_score_nvecs(xs)

Arguments

~~~~~~~~~

"xs"

list of vectors.

Value

~~~~~

Returns the log likelihood ratio.

Author(s)

~~~~~~~~~

Florent Chuffart

Examples

~~~~~~~~

   # LOD score for 2 set of values

   mean1=5; sd1=2; card2 = 250

   mean2=6; sd2=3; card1 = 200

   x1 = rnorm(card1, mean1, sd1)

   x2 = rnorm(card2, mean2, sd2)

   min = floor(min(c(x1,x2)))

   max = ceiling(max(c(x1,x2)))

   hist(c(x1,x2), xlim=c(min, max), breaks=min:max)

   lines(min:max,dnorm(min:max,mean1,sd1)*card1,col=2)

   lines(min:max,dnorm(min:max,mean2,sd2)*card2,col=3)

   lines(min:max,dnorm(min:max,mean(c(x1,x2)),sd(c(x1,x2)))*card2,col=4)

   llr_score_nvecs(list(x1,x2))

R: nm

nm

--

Description

~~~~~~~~~~~

It provides a set of useful functions allowing to perform quantitative

analysis of nucleosomal epigenome.

Details

~~~~~~~

+-----------------+-----------------------------------------------------+

| Package:        | nucleominer                                         |

+-----------------+-----------------------------------------------------+

| Maintainer:     | Florent Chuffart <florent.chuffart@ens-lyon.fr>     |

+-----------------+-----------------------------------------------------+

| Author:         | Florent Chuffart                                    |

+-----------------+-----------------------------------------------------+

| Version:        | 2.3.45                                              |

+-----------------+-----------------------------------------------------+

| License:        | CeCILL                                              |

+-----------------+-----------------------------------------------------+

| Title:          | nm                                                  |

+-----------------+-----------------------------------------------------+

| Depends:        | seqinr, plotrix, DESeq, cachecache                  |

+-----------------+-----------------------------------------------------+

Author(s)

~~~~~~~~~

Florent Chuffart

R: Plot the distribution of reads.

Plot the distribution of reads.

-------------------------------

Description

~~~~~~~~~~~

This fuxntion use the deseq nomalization feature to compare

qualitatively the distribution.

Usage

~~~~~

   plot_dist_samples(strain, marker, res, all_samples, NEWPLOT = TRUE)

Arguments

~~~~~~~~~

"strain"

The strain to considere.

"marker"