bbcflib::motif Namespace Reference

Functions
def	meme
def	parse_meme_xml
def	parallel_meme
def	motif_scan
def	save_motif_profile
def	FDR_threshold
def	sqlite_to_false_discovery_rate
Variables
tuple	track_result
list	fields = ['chr']
tuple	future = motif_scan.nonblocking( ex, fasta, motif, background, -100, stdout=output, via=via )
dictionary	shuf_futures = {}
tuple	out = unique_filename_in()
tuple	_ = future.wait()
dictionary	TP_scores = {}
int	ntp = 0
tuple	row = line.split("\t")
tuple	score = int(round(float(row[2])))
tuple	scores = sorted(TP_scores.keys(),reverse=True)
tuple	FP_scores = dict((k,0) for k in scores)
int	nfp = 0
tuple	fscore = int(round(float(row[2])))
tuple	tscore = max([k for k in scores if k<=fscore])
float	cur_fdr = 1.0
list	threshold = scores[0]

---

Detailed Description

=====================
Module: bbcflib.motif
=====================

No documentation

---

Function Documentation

def bbcflib::motif::FDR_threshold	(		ex,
			motif,
			background,
			assembly,
			regions,
			alpha = .1,
			nb_samples = 1,
			via = 'lsf'
	)

Computes a score threshold for 'motif' on 'regions' based on a false discovery rate < alpha and returns the
threshold or a dictionary with keys thresholds and values simulated FDRs when alpha < 0.

def bbcflib::motif::meme	(		fasta,
			outdir,
			maxsize = 10000000,
			args = None
	)

Binding for the ``meme`` motif finder.

def bbcflib::motif::motif_scan	(		fasta,
			motif,
			background,
			threshold = 0
	)

Binding for the ``S1K`` motif scanner.

def bbcflib::motif::parallel_meme	(		ex,
			assembly,
			regions,
			name = None,
			meme_args = None,
			via = 'lsf'
	)

Fetches sequences, then calls ``meme`` on them and finally saves the results in the repository.

def bbcflib::motif::parse_meme_xml	(		ex,
			meme_file,
			chrmeta
	)

Parse meme xml file and convert to track 

def bbcflib::motif::save_motif_profile	(		ex,
			motifs,
			background,
			assembly,
			regions,
			keep_max_only = False,
			threshold = 0,
			description = 'motif_scan.sql',
			via = 'lsf'
	)

Scan a set of motifs on a set of regions and saves the results as an sql file.
The 'motifs' argument is a single PWM file or a dictionary with keys motif names and values PWM files
with 'n' rows like:
"1 p(A) p(C) p(G) p(T)"
where the sum of the 'p's is 1 and the first column allows to skip a position with a '0'.

def bbcflib::motif::sqlite_to_false_discovery_rate	(		ex,
			motif,
			background,
			assembly,
			regions,
			alpha = 0.05,
			nb_samples = 1,
			description = '',
			via = 'lsf'
	)

Computes a score threshold for 'motif' on 'regions' based on a false discovery rate < alpha and returns the
thresholded profile.

---

Variable Documentation

tuple bbcflib::motif::track_result

Initial value:

track.track( sqlout, chrmeta=assembly.chrmeta,
                                info={'datatype':'qualitative'},
                                fields=['start','end','name','score','strand'] )