def __init__(self, fg_file, bg_file, cv_level, markov_file):
self.cv_level = cv_level
self.randomize = 0
self.beta = 0.0
self.delta = 0.001
self.refine = 1
self.motif_file = 'dummy.out'
self.dump = 0
self.family = ''
self.datafiles = (fg_file,bg_file)
MAX_FG = 2000
#LOAD MARKOV BACKGROUND#
print "Loading Markov background file from %s"%markov_file
EM.loadMarkovBackground(markov_file)
##################################################################################
#divide input sequences into groups according to the desired cross-validation level
###################################################################################
print "Processing input sequences...."
self.fg_seqs = Fasta.load(fg_file) #load foreground sequences
for key in self.fg_seqs.keys():
fseq = self.fg_seqs[key]
self.fg_seqs[key] = fseq.split()[0]
self.all_probes = Fasta.load(bg_file) #load background sequences
Fasta.delN(self.fg_seqs)
Fasta.delN(self.all_probes)
#first delete any sequences from background that are present in foreground
for key in self.fg_seqs.keys():
if (self.all_probes.has_key(key)):
del self.all_probes[key]
for key in self.all_probes.keys():
if ((len(self.all_probes[key])==0) or (re.search('[SWMKRY]', self.all_probes[key]))):
del self.all_probes[key]
print "deleting %s"%key
while (len(self.fg_seqs.keys())>MAX_FG):
del self.fg_seqs[self.fg_seqs.keys()[random.randint(0,(len(self.fg_seqs.keys())-1))]]
def main():
if len(sys.argv) < 2:
print "Usage: %s <fasta_file> [width = None ] [options]"%(re.sub('^.*/','',sys.argv[0]))
print "Options include:"
print ""
print " EM Parameters:"
print " -beta [0.01] Beta for pseudocounts"
print " -seedbeta[0.02] Beta for pseudocounts for seeds from text"
print " -gamma [0.2] Gamma (fraction of sequences)"
print " -delta [0.001] Convergence criteria"
print " "
print " Seeds (not actually proper priors)"
print " -prior Seqences or motifs for seeds (may be repeated)"
print " -top N [0] Include w-mers in top N probes"
print " -gap string sample gapped motifs"
# print " -TF Seed with (all) TRANSFAC PSSMs (buggy)"
print " -kmerseeds Use kmers with best enrichment score as seeds for EM"
print " -pad add NN..NN to seed"
print " "
print " Genome / Background model "
print " -human (250,1000) Use Human Background model"
print " -g genome.fsa Use specicied Fasta file as background (searches first for matching frequency file)"
# print " -Y2K, -Y5C Use Yeast Upstream Intergenic regions (2000, 500)"
# print " -B Use Bacterial Orfs"
print " "
print "Examples:"
print " %s t.fsa 5 -prior GGGTA -prior AAAAAC "%(sys.argv[0].split('/')[-1])
print " will start an EM with 3 seeds: GGGTA, AAAAA, and AAAAC"
print
print " %s t.fsa 5 -info CUP9.info -gamma 0.5 "%(sys.argv[0].split('/')[-1])
print " will start an EM with Enriched seeds in CUP9.info, with"
print " Gamma expectation of 50% of all probes"
print
print " %s t.fsa -prior MCM1_5.tamo:0 "%(sys.argv[0].split('/')[-1])
print " will start an EM with 0th motif of the file MCM1_5.tamo"
print " as a seed"
print
sys.exit(1)
fastafile = sys.argv[1]
#Echo the command line
print "#" + ' '.join(map(lambda x: re.sub(' ','\ ',x), sys.argv))
if sys.argv[2].isdigit():
width = sys.argv[2]
else: width = None
algorithm = ''
beta = ''
seedbeta = ''
deltamin = ''
gamma = 0.2
infofile = ''
seedmodels= []
species = 'YEAST'
valid_tfs = [] #NOT USED
gapped_syl= None
gapflank = 0
gapweight = 0.2
enrichfact= 0.7
pmax = 0 #False
TFSEEDS = 0
TFMids = []
pad = None
bgfile = None
seed_count = 0 #Default: Take the top 0
seed_s = [] #Initialize seq array
'''Parse command-line arguments'''
for tok,i in zip(sys.argv,xrange(len(sys.argv))):
if tok == '-top' : seed_count = int(sys.argv[i+1])
elif tok == '-greedy': algorithm = "GREEDY"
elif tok == '-prior' : seed_s.append(sys.argv[i+1])
elif tok == '-beta' : beta = float(sys.argv[i+1])
elif tok == '-seedbeta': seedbeta = float(sys.argv[i+1])
elif tok == '-gamma' : gamma = float(sys.argv[i+1])
elif tok == '-delta' : deltamin = float(sys.argv[i+1])
elif tok == '-kmerseeds' : infofile = 1
elif tok == '-valid' : valid_tfs.append(sys.argv[i+1]) #NOT USED
elif tok == '-w' : width = sys.argv[i+1]
elif tok == '-width' : width = sys.argv[i+1]
elif tok == '-gap' : gapped_syl = sys.argv[i+1]
elif tok == '-gapflank' :gapflank = int(sys.argv[i+1])
elif tok == '-gapweight':gapweight = float(sys.argv[i+1])
elif tok == '-enrichfact':enrichfact= float(sys.argv[i+1])
elif tok == '-pmax' : pmax = 1
elif tok == '-Y2K' : species = "YEAST_2000_UP"
elif tok == '-Y5C' : species = "YEAST_500_UP"
elif tok == '-B' : species = "BAC_ORF"
elif tok == '-Ch22' : species = "Ch22"
elif tok == '-genome': species = sys.argv[i+1]
elif tok == '-pad' : pad = "TRUE"
elif tok == '-bgfile': bgfile = sys.argv[i+1]
elif tok == '-TF' : #NOT USED (TRANSFAC NOT SUPPLIED WITH DISTRIBUTION)
TFSEEDS = 1
for j in range(i+1,len(sys.argv)):
if re.match('M0',sys.argv[j]):
TFMids.append(sys.argv[j])
else:
break
elif tok == '-human' :
_s = ''
if sys.argv[i+1].isdigit(): _s = '_'+sys.argv[i+1]
else: _s = ''
species = 'HUMAN'+_s
if infofile: infofile = fastafile
if bgfile:
EM.loadMarkovBackground(bgfile)
elif not ('-random_background' in sys.argv or '-nomarkov' in sys.argv):
EM.loadMarkovBackground(species)
else:
EM.theMarkovBackground = EM.Zeroth()
fsaD = Fasta.load(fastafile)
Fasta.delN(fsaD)
seqs = fsaD.values()
probes = fsaD.keys()
all_seqs = seqs
seed_s.extend(seqs[0:min(seed_count,len(seqs))])
if infofile and width=='info':
width = info2width(infofile)
elif width != None:
width = int(width)
#Alternate source of seeds
if infofile:
if 1 or width:
seedmodels.extend(info2seeds(width,infofile,fastafile,species))
else:
print 'Error: need to specify motif width w/ .info file'
#Any -prior pointers to motifs in other files?
(seed_s, motifs) = parse_priors(seed_s)
seedmodels.extend(motifs)
#Should we get seeds from TRANSFAC?
if TFSEEDS: #NOT USED
tf = []
D = tfmats()
if not TFMids:
keys = D.keys()
else:
keys = []
for TFMid in TFMids:
for key in D.keys():
if key[0:6] == TFMid:
keys.append(key)
break
for key in keys:
m = D[key]
m.seednum = int(re.sub('M0*','',key.split()[0]))
m.seedtxt = '%-24s %s'%(m,key)
tf.append(m)
tf.sort(lambda x,y: cmp(x.seednum,y.seednum))
seedmodels.extend(tf)
#seedmodels.append(tf[33])
if gapped_syl:
gapped_priors = gapped_motifs(gapped_syl)
gapped_priors = map(lambda x:'N'+x+'N', gapped_priors)
seed_s.extend(gapped_priors)
if pad:
print '# Padding models with NN-m-NN'
newmodels = []
left = MotifTools.Motif_from_text('@')
right = MotifTools.Motif_from_text('N')
for m in seedmodels:
newmodels.append(left + m + right)
print left + m + right
seedmodels = newmodels
'''
Set everything up and GO!!
'''
global theEM
theEM = EM.EM(seed_s,[],width,"VERBOSE")
if beta: theEM.beta = beta
if deltamin: theEM.deltamin = deltamin
if seedbeta: theEM.seedbeta = seedbeta
theEM.param['gamma'] = gamma
theEM.seqs.extend(all_seqs)
theEM.models = seedmodels
theEM.gapflank = gapflank
theEM.gapweight = gapweight
theEM.report()
theEM.EM_Cstart() #GO!!
#print "#Sorting candidates"
#sys.stdout.flush()
#EM.candidates.sort(lambda x,y: cmp(y.MAP,x.MAP))
'''
Compute some metrics
'''
print "#Loading Genome %s"%species ; sys.stdout.flush()
Genome = ProbeSet(species,enrichfact)
ids = Genome.ids_from_file(fastafile)
for C in theEM.candidates:
if not pmax:
C.pssm.pvalue = Genome.p_value(C.pssm,ids,'verbose')
C.pssm.church = Genome.church(C.pssm,ids)
C.pssm.frac = Genome.frac(C.pssm,probes,None,0.7)
else:
(p,frac) = Genome.best_p_value(C.pssm,ids)
C.pssm.pvalue = p
C.pssm.threshold = frac * C.pssm.maxscore
print "Bests:",p,frac
matching = Genome.matching_ids(C.pssm,[],factor=0.7)
matchbound = [x for x in matching if x in probes]
C.pssm.numbound = len(probes)
C.pssm.nummotif = len(matching)
C.pssm.numboundmotif = len(matchbound)
sys.stdout.flush()
'''
Print out all motifs (sorted by Enrichment) in an AlignACE-like form
'''
theEM.candidates.sort(lambda x,y: cmp(x.pssm.pvalue,y.pssm.pvalue))
for C,i in zip(theEM.candidates,range(len(theEM.candidates))):
C.pssm.maxscore = -100 #May have side effects. Recompute when done
if C.pssm.valid: #NOT USED
_t = C.pssm.valid
if not _t[0]:
vstring = "(--- %8.4f %8.4f %s)"%(_t[1],_t[2],_t[3])
else:
vstring = "(HIT %8.4f %8.4f %s)"%(_t[1],_t[2],_t[3])
else:
vstring = ''
C.pssm._maxscore() #Recomputed
MotifTools.print_motif(C.pssm,20,i)
sys.stdout.flush()
continue
#Antiquated stuff -- Remove !!
print "Log-odds matrix for Motif %3d %s"%(i,C)
C.pssm._print_ll()
print "Sequence Logo"
C.pssm._print_bits()
flush()
#print '# %3d matching sequences at 90%%'%len(C.pssm.bestseqs(C.pssm.maxscore * 0.9))
flush()
m = C.pssm
if not m.__dict__.has_key('gamma'): m.gamma = None #Kludge to deal w/ old shelves
if m.seedtxt: print "Seed: %3d %s"%(i,m.seedtxt)
if m.source: print "Source: ",m.source
if m.gamma: print "Gamma: %7.5f"%m.gamma
if m.threshold: print "Threshold: %5.2f"%m.threshold
#if C.pssm.seedtxt:
# print 'Seed %3d %-25s'%(i,C.pssm.seedtxt)
if C.pssm.church != None: vstring = 'ch: %5.2f %s'%(
math.fabs(math.log(C.pssm.church)/math.log(10)), vstring)
print "Motif %3d %-25s nlog(p): %6.3f %s"%(i,C,-math.log(C.pssm.pvalue)/math.log(10),vstring)
if C.pssm.threshold:
print "Threshold: %6.3f %4.1f%%"%(
C.pssm.threshold, 100.0*C.pssm.threshold/C.pssm.maxscore)
C.pssm.maxscore = -1e100 #May have side effects. Recompute when done
for seq in C.wmers:
print seq,i,C.pssm.scan(seq)[2][0]
C.pssm._maxscore() #Recomputed
print '*'*len(seq)
print "MAP Score: %f"%C.MAP
sys.stdout.flush()
sys.stdout.flush()
sys.exit(0) #Avoid ridiculous python cleanup times
