LBMC » NucleoMiner

Python and R Common Configuration File

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

First of all we define in one place some configuration variables that

will be launch by python and R scripts. This file is

**configurator.py**. The execution of this python script dumps

variables into the **nucleo_miner_config.json** that will be launch by

both kind of scriopts (R and puython).

To do this launch at the root of your project the following command

line:

   python src/current/configurator.py

$$$ other python script to describe: - libcoverage.py - wf.py

Dataset and Configuration Variables

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

In order to simplify the design of experiment, we considere Mnase as a

configurator.CSV_SAMPLE_FILE = None

   Path to cvs file that contains sample information.

configurator.ILLUMINA_OUTPUTFILE_PREFIX = None

   Prefix for Illumina fastq output files.

configurator.FASTA_REFERENCE_GENOME_FILES = None

   Dictionary where each fasta reference genomes is indexed by

   reference strain that it corresponds.

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.AREA_BLACK_LIST = None

   Dictionary where keys are strain and values are black listed of

   geneome region.

configurator.C2C_FILES = None

   Dictionary where each strain combination indexes genome aligment.

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.

     for strain in strains:

       per_strain_stats[strain] = create_bowtie_index(strain, 

         config["FASTA_REFERENCE_GENOME_FILES"][strain], config["INDEX_DIR"], 

         config["BOWTIE_BUILD_BIN"])

     for sample in samples:

       per_sample_align_stats["sample_%s" % sample["id"]] = align_reads(sample, 

         config["ALIGN_DIR"], config["LOG_DIR"], config["INDEX_DIR"], 

         config["ILLUMINA_OUTPUTFILE_PREFIX"], config["BOWTIE2_BIN"], 

         config["SAMTOOLS_BIN"], config["BEDTOOLS_BIN"])

     for sample in samples:

       per_sample_convert_stats["sample_%s" % sample["id"]] = split_fr_4_TF(sample, 

         config["ALIGN_DIR"], config["FASTA_INDEXES"], config["AREA_BLACK_LIST"], 

         config["READ_LENGTH"],config["MAPQ_THRES"])

For some reasons (manipulation efficiency, e.g. PCR...), we remove

     for sample in samples_mnase:

       per_mnase_sample_stats["sample_%s" % sample["id"]] = template_filter(sample, 

         config["ALIGN_DIR"], config["LOG_DIR"], config["TF_BIN"], 

         config["TF_TEMPLATES_FILE"], config["TF_CORR"], config["TF_MINW"], 

         config["TF_MAXW"], config["TF_OL"])  

The second part of the tutorial uses *R*

(http://http://www.r-project.org). It consists in a set of R scripts

taht will be sourced in an R console launched at the root of your

project. the R srcipts are:

   * headers.R

The Script headers.R

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

The script header.R is included in each other scripts. It is in charge

of:

   * launching libraries used in thes scripts

   * launching configuration (design, strain, marker...)

   * computing and caching CURs

In your R console, run the following command line:

   R CMD BATCH src/current/header.R

The Script extract_maps.R

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

This script is in charge of extracting Maps for well positioned and

fuzzy nucleosomes. First of all, this script computed intra and inter

strain nucleosome maps for each CUR. This step is executed in parallel

on many cores using the BoT library. Next, it collects results and

produces well positioned, fuzzy and UNR maps.

The well-positioned map for BY is collected in the result directory

and is called **BY_wp.tab**. It is composed of following columns:

   * chr, the number of the chromosome

   * lower_bound, the lower bound of the nucleosome

   * upper_bound, the upper bound of the nucleosome

   * cur_index, index of the CUR

   * index_nuc, the index of the nucleosome in the CUR

   * wp, 1 if it is a well positioned nucleosome, 0 else

   * nb_reads, the number of reads that supports this nucleosome

   * nb_nucs, the number of TemplateFilter nucleosome across

     replicates (= the number of replicates if it is a well-positioned

     nucleosome)

   * llr_1, for a well-positioned nucleosome, it is the LLR1 between

     the first and the second TemplateFilter nucleosome.

   * llr_2, for a well-positioned nucleosome, it is the LLR1 between

     the second and the first TemplateFilter nucleosome.

   * wp_llr, for a well-positioned nucleosome, it is the LLR2

     overall TemplateFilter nucleosomes.

   * wp_pval, for a well-positioned nucleosome, it is the p-value

     chi square test obtained with the LLR2 (**1-pchisq(2.LLR2,

     df=4)**)

   * dyad_shift, for a well-positioned nucleosome, it is shift

     between the two extreme TemplateFilter nucleosome dyad positions.

The fuzzy map for BY is collected in the result directory and is

called **BY_fuzzy.tab**. It is composed of following columns:

   * chr, the number of the chromosome

   * lower_bound, the lower bound of the nucleosome

   * upper_bound, the upper bound of the nucleosome

   * cur_index, index of the CUR

The common well-position map for BY and RM strains is collected in the

result directory and is called **BY_RM_common_wp.tab**. It is composed

of following columns:

   * cur_index, the index of the CUR

   * index_nuc_BY, the index of the BY nucleosome in the CUR

   * index_nuc_RM,the index of the RM nucleosome in the CUR

   * llr_score, the LLR3 score between th eBy and RM nucleosomes

   * common_wp_pval,  the p-value chi square test obtained with the

     LLR3 (**1-pchisq(2.LLR3, df=2)**)

The common UNR map for BY and RM strains is collected in the result

directory and is called **BY_RM_common_unr.tab**. It is composed of

following columns:

   * cur_index, the index of the CUR

   * index_nuc_BY, the index of the BY nucleosome in the CUR

   * index_nuc_RM,the index of the RM nucleosome in the CUR

To execute this script, run the following command line in your R

console:

   source("src/current/extract_maps.R")

The Script compare_common_wp.R

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

This script is used to compare inter strain distances between common

well-positioned nucleosomes.

For example, it compute the file **BY_RM_common_wp_diff.tab** that

contains dyad shifts between two well-positioned nucleosomes. It is

composed of following columns:

   * cur_index, the index of the CUR

   * index_nuc_BY, the index of the BY nucleosome in the CUR

   * index_nuc_RM,the index of the RM nucleosome in the CUR

   * llr_score, the LLR3 score between th eBy and RM nucleosomes

   * common_wp_pval,  the p-value chi square test obtained with the

     LLR3 (**1-pchisq(2.LLR3, df=2)**)

   * diff, the dyad shifts between two well-positioned nucleosomes

It also translates well-positioned nucleosome maps from a strain to an

other strain and stores it into a table.

For example, the file **results/2014-04/RM_wp_tr_2_BY.tab** contains

RM well-positioned nucleosome translated into the BY genome

referential. It is composed of following columns:

   * strain_ref, the reference genome (in which positioned are

     defined)

   * begin, the translated lower bound of the nucleosome

   * end, the translated upper bound of the nucleosome

   * chr, the number of chromosome for the reference genome (in

     which positioned are defined)

   * length, the length of the nucleosome (could be negative)

   * cur_index, the index of the CUR

   * index_nuc, the index of the nucleosome in the CUR

To execute this script, run the following command line in your R

console:

wpfuzzy} considering well positioned nucleosomes, fuzzy nucleosomes or

both for SNEP computation.