bbcflib::c4seq Namespace Reference

Functions
def	loadPrimers
def	segToFrag
def	profileCorrection
def	smoothFragFile
def	runDomainogram
def	density_to_countsPerFrag
def	workflow_groups
Variables
dictionary	processed = {'lib': {}, 'density': {}, '4cseq': {}}
dictionary	regToExclude = {}
list	new_libs = []
	job_groups = job.groups
tuple	htss_mapseq = frontend.Frontend( url=mapseq_url )
dictionary	run_domainogram = {}
tuple	before_profile_correction = group.get('before_profile_correction',False)
	via = via)
list	density_files = []
list	libname = mapseq_files[gid]
tuple	density_file
tuple	description
dictionary	futures = {}
tuple	file1 = unique_filename_in()
tuple	file2 = unique_filename_in()
tuple	file3 = unique_filename_in()
list	nFragsPerWin = group['window_size']
tuple	resfile = unique_filename_in()
dictionary	futures2 = {}
list	profileCorrectedFile = processed['4cseq']
list	bedGraph = processed['4cseq']
list	grName = job_groups[gid]
tuple	file4 = unique_filename_in()
list	regCoord = regToExclude[gid]
int	script_path = 10
list	resFiles = []
list	logFile = f[1]
	start = False
list	tarname = job_groups[gid]
tuple	res_tar = tarfile.open(tarname, "w:gz")
tuple	s = s.strip()
string	step = "density"
string	fname = "density_file_"
string	groupId = "sql"
tuple	wig = unique_filename_in()
string	comment = "all informative frags - null included"
tuple	trsql = track.track(resfiles[3])
tuple	bwig = unique_filename_in()
tuple	trwig = track.track(bwig,chrmeta=trsql.chrmeta)
dictionary	selection = {'score':(0.01,sys.maxint)}
list	reportProfileCorrection = resfiles[1]
list	smoothFile = resfiles[0]
list	afterProfileCorrection = resfiles[1]
tuple	nFrags = str(job_groups[gid]['window_size'])
tuple	tarFile = resfiles.pop()

---

Detailed Description

=======================
Module: bbcflib.c4seq
=======================

This module provides functions to run a 4c-seq analysis 
from reads mapped on a reference genome.

---

Function Documentation

def bbcflib::c4seq::density_to_countsPerFrag	(		ex,
			file_dict,
			groups,
			assembly,
			regToExclude,
			script_path,
			via = 'lsf'
	)

Main function to compute normalised counts per fragments from a density file.

def bbcflib::c4seq::loadPrimers

(

primersFile

)

Create a dictionary with infos for each primer (from file primers.fa) 

def bbcflib::c4seq::segToFrag	(		countsPerFragFile,
			regToExclude = "",
			script_path = ''
	)

This function calls segToFrag.awk (which transforms the counts per segment to a normalised count per fragment).
Provide a region to exclude if needed. 

def bbcflib::c4seq::workflow_groups	(		ex,
			job,
			primers_dict,
			assembly,
			mapseq_files,
			mapseq_url,
			c4_url = None,
			script_path = '',
			logfile = None,
			via = 'lsf'
	)

Main 
* open the 4C-seq minilims and create execution
* 0. get/create the library 
* 1. if necessary, calculate the density file from the bam file (mapseq.parallel_density_sql)
* 2. calculate the count per fragment for each denstiy file with gFeatMiner:mean_score_by_feature to calculate)

---

Variable Documentation

list bbcflib::c4seq::density_file

Initial value:

parallel_density_sql( ex, mapseq_files[gid][rid]['bam'],
                                                   assembly.chromosomes,
                                                   nreads=mapseq_files[gid][rid]['stats']["total"],
                                                   merge=0,
                                                   convert=False,
                                                   via=via )

tuple bbcflib::c4seq::description

Initial value:

set_file_descr("density_file_"+libname+".sql",
                                                   groupId=gid,step="density",type="sql",view='admin',gdv="1")