def tamo2tamo(file, outname):
global probefile, PROBESET, fsafile
motifs = MotifTools.load(file)
if fsafile:
fsaname = fsafile
else:
fsaname = find_fsa(file)
print '# FSA ', fsaname
fsaD = MotifMetrics.fasta2seqs(fsaname, 'want_dict')
probes = fsaD.keys()
if not probefile:
PROBESET = MotifMetrics.ProbeSet('YEAST')
#PROBESET= pick_genome(fsaname)
#for key,seq in fsaD.items():
# PROBESET.probes[key] = seq
print "# %d motifs" % len(motifs)
for motif in motifs:
#motif.pvalue, motif.church = 1,1 #Comment this!
if motif.pvalue == 1:
motif.pvalue = PROBESET.p_value(motif, probes, 'v')
if motif.church == 1:
motif.church = PROBESET.church(motif, probes, 'v')
#if motif.E_site == None: motif.E_site = PROBESET.E_sitef(motif,probes,3,'v')
#if motif.E_chi2 == None: motif.E_chi2 = PROBESET.E_chi2(motif,probes,None,'v')
#if motif.E_seq == None: motif.E_seq = PROBESET.E_seq(motif,probes,'v')
if motif.ROC_auc == None:
motif.ROC_auc = PROBESET.ROC_AUC(motif, probes, 'v')
#if motif.MNCP == None: motif.MNCP = PROBESET.MNCP(motif,probes,'v')
if motif.frac == None:
motif.frac = PROBESET.frac(motif, probes, 'v', 0.7)
if motif.numbound == 0:
matching = PROBESET.matching_ids(motif, [], factor=0.7)
matchbound = [x for x in matching if x in probes]
motif.numbound = len(probes)
motif.nummotif = len(matching)
motif.numboundmotif = len(matchbound)
if 0 and motif.CRA == None:
try:
pass
CRA, Cfrac = PROBESET.cons_ROC_AUC(motif,
probes,
'v',
tuple='YES')
motif.CRA = CRA
motif.Cfrac = Cfrac
except:
pass
MotifTools.save_motifs(motifs, outname)
def tamo2tamo(file, outname):
global probefile, PROBESET, fsafile
motifs = MotifTools.load(file)
if fsafile:
fsaname = fsafile
else:
fsaname = find_fsa(file)
print '# FSA ',fsaname
fsaD = MotifMetrics.fasta2seqs(fsaname,'want_dict')
probes = fsaD.keys()
if not probefile:
PROBESET = MotifMetrics.ProbeSet('YEAST')
#PROBESET= pick_genome(fsaname)
#for key,seq in fsaD.items():
# PROBESET.probes[key] = seq
print "# %d motifs"%len(motifs)
for motif in motifs:
#motif.pvalue, motif.church = 1,1 #Comment this!
if motif.pvalue == 1: motif.pvalue = PROBESET.p_value(motif,probes,'v')
if motif.church == 1: motif.church = PROBESET.church(motif,probes,'v')
#if motif.E_site == None: motif.E_site = PROBESET.E_sitef(motif,probes,3,'v')
#if motif.E_chi2 == None: motif.E_chi2 = PROBESET.E_chi2(motif,probes,None,'v')
#if motif.E_seq == None: motif.E_seq = PROBESET.E_seq(motif,probes,'v')
if motif.ROC_auc== None: motif.ROC_auc= PROBESET.ROC_AUC(motif,probes,'v')
#if motif.MNCP == None: motif.MNCP = PROBESET.MNCP(motif,probes,'v')
if motif.frac == None: motif.frac = PROBESET.frac(motif,probes,'v',0.7)
if motif.numbound == 0:
matching = PROBESET.matching_ids(motif,[],factor=0.7)
matchbound = [x for x in matching if x in probes]
motif.numbound = len(probes)
motif.nummotif = len(matching)
motif.numboundmotif = len(matchbound)
if 0 and motif.CRA == None:
try:
pass
CRA, Cfrac = PROBESET.cons_ROC_AUC(motif,probes,'v',tuple='YES')
motif.CRA = CRA
motif.Cfrac = Cfrac
except: pass
MotifTools.save_motifs(motifs,outname)
def ace2tamo(filename, tamoname):
global probefile, PROBESET
if re.search('\.ace$',filename):
mdobject = AlignAce.AlignAce(filename)
elif re.search('\.meme$',filename):
mdobject = Meme.Meme(filename)
fsaname = find_fsa(mdobject.fastafile)
fsaD = MotifMetrics.fasta2seqs(fsaname,'want_dict')
probes = fsaD.keys()
if not probefile:
PROBESET = MotifMetrics.ProbeSet('HUMAN_250')
#PROBESET= pick_genome(fsaname)
for key,seq in fsaD.items():
PROBESET.probes[key] = seq
for motif in mdobject.motifs:
if motif.pvalue == 1: motif.pvalue = PROBESET.p_value(motif,probes,'v')
if motif.church == 1: motif.church = PROBESET.church(motif,probes,'v')
if motif.E_site == None: motif.E_site = PROBESET.E_sitef(motif,probes,3,'v')
#if motif.E_chi2 == None: motif.E_chi2 = PROBESET.E_chi2(motif,probes,None,'v')
if motif.E_seq == None: motif.E_seq = PROBESET.E_seq(motif,probes,'v')
if motif.ROC_auc== None: motif.ROC_auc= PROBESET.ROC_AUC(motif,probes,'v')
if motif.MNCP == None: motif.MNCP = PROBESET.MNCP(motif,probes,'v')
if re.search('\.meme$',filename):
motif.MAP = -math.log(motif.evalue)/math.log(10)
sys.stdout.flush()
i = 0
for motif in mdobject.motifs:
motif.seednum = i ; i=i+1
kmers = motif.bogus_kmers(100)
motif.maxscore = -100
scores = [motif.scan(kmer)[2][0] for kmer in kmers]
print Arith.avestd(scores)
if re.search('\.meme$',filename):
mdobject.motifs.sort(lambda x,y: cmp(x.pvalue, y.pvalue))
else:
mdobject.motifs.sort(lambda x,y: cmp(x.church, y.church))
MotifTools.save_motifs(mdobject.motifs,tamoname)
def ace2tamo(filename, tamoname):
global probefile, PROBESET
if re.search('\.ace$',filename):
mdobject = AlignAce.AlignAce(filename)
elif re.search('\.meme$',filename):
mdobject = Meme.Meme(filename)
fsaname = find_fsa(mdobject.fastafile)
fsaD = MotifMetrics.fasta2seqs(fsaname,'want_dict')
probes = fsaD.keys()
if not probefile:
PROBESET = MotifMetrics.ProbeSet('HUMAN_250')
#PROBESET= pick_genome(fsaname)
for key,seq in fsaD.items():
PROBESET.probes[key] = seq
for motif in mdobject.motifs:
if motif.pvalue == 1: motif.pvalue = PROBESET.p_value(motif,probes,'v')
if motif.church == 1: motif.church = PROBESET.church(motif,probes,'v')
if motif.E_site == None: motif.E_site = PROBESET.E_sitef(motif,probes,3,'v')
#if motif.E_chi2 == None: motif.E_chi2 = PROBESET.E_chi2(motif,probes,None,'v')
if motif.E_seq == None: motif.E_seq = PROBESET.E_seq(motif,probes,'v')
if motif.ROC_auc== None: motif.ROC_auc= PROBESET.ROC_AUC(motif,probes,'v')
if motif.MNCP == None: motif.MNCP = PROBESET.MNCP(motif,probes,'v')
if re.search('\.meme$',filename):
motif.MAP = -math.log(motif.evalue)/math.log(10)
sys.stdout.flush()
i = 0
for motif in mdobject.motifs:
motif.seednum = i ; i=i+1
kmers = motif.bogus_kmers(100)
motif.maxscore = -100
scores = [motif.scan(kmer)[2][0] for kmer in kmers]
print Arith.avestd(scores)
if re.search('\.meme$',filename):
mdobject.motifs.sort(lambda x,y: cmp(x.pvalue, y.pvalue))
else:
mdobject.motifs.sort(lambda x,y: cmp(x.church, y.church))
MotifTools.save_motifs(mdobject.motifs,tamoname)
def motifs2tamo(motifs, outname):
global probefile, PROBESET
fsaname = find_fsa(outname)
fsaD = MotifMetrics.fasta2seqs(fsaname,'want_dict')
probes = fsaD.keys()
if not probefile:
PROBESET = MotifMetrics.ProbeSet('YEAST')
#PROBESET= pick_genome(fsaname)
#for key,seq in fsaD.items():
# PROBESET.probes[key] = seq
print "# %d motifs"%len(motifs)
for motif in motifs:
if motif.pvalue == 1: motif.pvalue = PROBESET.p_value(motif,probes,'v')
if motif.church == 1: motif.church = PROBESET.church(motif,probes,'v')
if motif.E_site == None: motif.E_site = PROBESET.E_sitef(motif,probes,3,'v')
#if motif.E_chi2 == None: motif.E_chi2 = PROBESET.E_chi2(motif,probes,None,'v')
if motif.E_seq == None: motif.E_seq = PROBESET.E_seq(motif,probes,'v')
if motif.ROC_auc== None: motif.ROC_auc= PROBESET.ROC_AUC(motif,probes,'v')
if motif.MNCP == None: motif.MNCP = PROBESET.MNCP(motif,probes,'v')
MotifTools.save_motifs(motifs,outname)
