Révision 21b8928f doc/sphinx_doc/build/text/tuto.txt

b/doc/sphinx_doc/build/text/tuto.txt
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the 53 samples is indentify by a uniq identifier. The file
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*CSV_SAMPLE_FILE* sums up this information.
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configurator.CSV_SAMPLE_FILE = None


  		





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   Path to cvs file that contains sample information.


  		





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We use a convention to link sample and Illumina fastq outputs.


  		





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Illumina output files of the sample *ID* will be stored in the


  		





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directory *ILLUMINA_OUTPUTFILE_PREFIX* + *ID*. For example, sample 41


  		





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outputs will be stored in the directory


  		





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*data/2012-09-05/FASTQ/Sample_Yvert_Bq41/*.


  		





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configurator.ILLUMINA_OUTPUTFILE_PREFIX = None


  		





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   Prefix for Illumina fastq output files.


  		





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For BY (resp. RM and YJM) we use following reference genome


  		





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*saccharomyces_cerevisiae_BY_S288c_chromosomes.fasta* (resp.


  		





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*saccharomyces_cerevisiae_rm11-1a_1_supercontigs.fasta* and


  		





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*saccharomyces_cerevisiae_YJM_789_screencontig.fasta*). The index


  		





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*FASTA_REFERENCE_GENOME_FILES* stores this information.


  		





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configurator.FASTA_REFERENCE_GENOME_FILES = None


  		





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   Dictionary where each fasta reference genomes is indexed by


  		





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   reference strain that it corresponds.


  		





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Each chromosome/contig is identify in the fasta file by an obscure


  		





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identifier. For example, BY chromosome I is identify by


  		





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*gi|144228165|ref|NC_001133.7|* when TemplateFilter is waiting for an


  		





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integer. So, we translate it. The index *FASTA_INDEXES* stores this


  		





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translation.


  		





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configurator.FASTA_INDEXES = None


  		





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   Dictionary of strain that indexes dictionaries where keys are


  		





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   chromosome reference from Fastq file and value are its


  		





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   correspondance for Templatefilter.


  		





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From a pragamatical point of view we discard some part of the genome


  		





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(repeated sequence etc...). The list of the black listed area is


  		





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explicitely detailled in *AREA_BLACK_LIST*.


  		





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configurator.AREA_BLACK_LIST = None


  		





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   Dictionary where keys are strain and values are black listed of


  		





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   geneome region.


  		





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For BY-RM (resp. BY-YJM and RM-YJM) genome sequence alignment we use


  		





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previously compute .c2c file


  		





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*data/2012-03_primarydata/BY_RM_gxcomp.c2c* (resp.


  		





  ......




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*NucleoMiner*, the old version of *NucleoMiner2* (http://www.ens-


  		





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lyon.fr/LBMC/gisv/NucleoMiner_Manual/manual.pdf).


  		





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configurator.C2C_FILES = None


  		





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   Dictionary where each strain combination indexes genome aligment.


  		





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*nucleominer* uses specific directory to work in, these are described


  		





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in *INDEX_DIR*, *ALIGN_DIR* and *LOG_DIR*.


  		





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  ......




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All paths, prefixes and indexes could be change in the


  		





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*src/current/nucleominer_config.json* file.


  		





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wf.json_conf_file = 'src/nucleo_miner/nucleo_miner_config.json'


  		





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   Path to the json configuration file.


  		





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Preprocessing Illumina Fastq Reads for Each Sample


  		





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


  		





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*samples* *samples_mnase* and *strains* that will be used along the 4


  		





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steps.


  		





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wf.samples = []


  		





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


  		





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   a strain name (key *strain*).


  		





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wf.samples_mnase = []


  		





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   List of Mnase samples.


  		





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wf.strains = []


  		





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   List of reference strains.


  		





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Creating Bowtie Index from each Reference Genome


  		





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


  		





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will be used by bowtie to align reads. This step is performed by the


  		





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following part of the *wf.py* script:


  		





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     for strain in strains:


  		





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       per_strain_stats[strain] = create_bowtie_index(strain, 


  		





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


  		





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         config["BOWTIE_BUILD_BIN"])


  		





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The following table sum up involved file sizes and process durations


  		





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concerning this step.


  		





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*subprocess* class. This step is performed by the followinw part of


  		





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the *wf.py* script:


  		





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     for sample in samples:


  		





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


  		





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


  		





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


  		





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


  		





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Convert Aligned Reads for TemplateFilter


  		





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


  		





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This step is performed by the followinw part of the *wf.py* script:


  		





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     for sample in samples:


  		





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


  		





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


  		





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


  		





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The following table sum up number of reads, involved file sizes and


  		





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process durations concerning the two last steps. In our case, aligment


  		





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process have been multuthreaded over over 3 cores.


  		





  ......




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This step is performed by the followinw part of the *wf.py* script:


  		





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     for sample in samples_mnase:


  		





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


  		





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


  		





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


  		





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


  		





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


  		





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| id | strain | found nucs | nuc file size | process duration |


  		





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+====+========+============+===============+==================+


  		












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