LBMC » NucleoMiner

References

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Python Reference

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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/nucleo_miner/nucleo_miner_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 *lod_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, lod_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=...)*.

"lod_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 lod

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, lod_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.

"lod_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 lod

scores.

Author(s)

~~~~~~~~~

Florent Chuffart

Examples

~~~~~~~~

       # Define new translate_roi function...

       translate_roi = function(roi, strain2, big_roi=NULL, config=NULL) {

         return(roi)

       }

       # Binding it by uncomment follwing lines.

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

       unlockBinding("translate_roi", getNamespace("nucleominer"))

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

       assign("translate_roi", translate_roi, getNamespace("nucleominer"))

       lockBinding("translate_roi", getNamespace("nucleominer"))

       lockBinding("translate_roi", 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_roi(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: 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 = 10, min_cur_width = 200,

       config = NULL, plot = FALSE)

Arguments

~~~~~~~~~

"diff_allowed"

the maximum indel width allowe din a CUR

"min_cur_width"

The minimum width of a CUR

"config"

GLOBAL config variable

"plot"

Plot CURs or not

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_roi" bug in

translate_roi 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: Extract wp nucs from nuc map.

Extract wp nucs from nuc map.

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

Description

~~~~~~~~~~~

Function based on common wp nuc index and roi_index.

Usage

~~~~~

   extract_wp(strain_maps, roi_index, strain, tmp_common_nucs)

Arguments

~~~~~~~~~

"strain_maps"

Nuc maps.

"roi_index"

The region of interest index.

"strain"

The strain to consider.

"tmp_common_nucs"

the list of wp nucs.

Author(s)

~~~~~~~~~

Florent Chuffart

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)

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.

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 overlap

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.