def SGDData():
root = TAMO.paths.SGDdir
urlroot = 'ftp://genome-ftp.stanford.edu/pub/yeast/data_download/'
files = ['chromosomal_feature/SGD_features.tab',
'chromosomal_feature/dbxref.tab',
'chromosomal_feature/chromosome_length.tab',
'sequence/GenBank/yeast_nrpep.fasta.gz',
'sequence/genomic_sequence/orf_protein/orf_trans_all.fasta.gz',
('http://yeastgfp.ucsf.edu/allOrfData.txt','Huh_Nature_2003.tab')
]
chrs = '01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 mt'.split()
files.extend( ['sequence/NCBI_genome_source/chr%s.fsa'%x for x in chrs] )
downloadfiles(root,urlroot,files)
from TAMO.seq import Fasta
print "Assembling yeast genome sequence files into a single file (NCBI_yeast_genome.fsa)"
D = {}
for chr in chrs:
_d = Fasta.load('%s/chr%s.fsa'%(TAMO.paths.SGDdir,chr))
id, seq = _d.items()[0]
if chr[0] == '0': chr = chr[1]
D['chr%s %s'%(chr,id)] = seq
Fasta.write(D, TAMO.paths.SGDdir + 'NCBI_yeast_genome.fsa')
def SGDData():
root = TAMO.paths.SGDdir
urlroot = 'ftp://genome-ftp.stanford.edu/pub/yeast/data_download/'
files = ['chromosomal_feature/SGD_features.tab',
'chromosomal_feature/dbxref.tab',
'chromosomal_feature/chromosome_length.tab',
'sequence/GenBank/yeast_nrpep.fasta.gz',
'sequence/genomic_sequence/orf_protein/orf_trans_all.fasta.gz',
('http://yeastgfp.ucsf.edu/allOrfData.txt','Huh_Nature_2003.tab')
]
chrs = '01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 mt'.split()
files.extend( ['sequence/NCBI_genome_source/chr%s.fsa'%x for x in chrs] )
downloadfiles(root,urlroot,files)
from TAMO.seq import Fasta
print "Assembling yeast genome sequence files into a single file (NCBI_yeast_genome.fsa)"
D = {}
for chr in chrs:
_d = Fasta.load('%s/chr%s.fsa'%(TAMO.paths.SGDdir,chr))
id, seq = _d.items()[0]
if chr[0] == '0': chr = chr[1]
D['chr%s %s'%(chr,id)] = seq
Fasta.write(D, TAMO.paths.SGDdir + 'NCBI_yeast_genome.fsa')
def genomebg(infile,outfile):
EXE = MDSCAN_DIR + 'genomebg.linux'
fsaD = Fasta.load(infile)
tmpfsa = tempfile.mktemp()
Fasta.write(fsaD,tmpfsa,linelen=1000000000)
CMD = '%s -i %s -o %s'%(EXE,tmpfsa,outfile)
FID = os.popen('( %s ;) 2>&1'%CMD,'r')
for line in FID.readlines(): print line
if FID.close(): print "Exited"
os.unlink(tmpfsa)
def genomebg(infile, outfile):
EXE = MDSCAN_DIR + 'genomebg.linux'
fsaD = Fasta.load(infile)
tmpfsa = tempfile.mktemp()
Fasta.write(fsaD, tmpfsa, linelen=1000000000)
CMD = '%s -i %s -o %s' % (EXE, tmpfsa, outfile)
FID = os.popen('( %s ;) 2>&1' % CMD, 'r')
for line in FID.readlines():
print line
if FID.close(): print "Exited"
os.unlink(tmpfsa)
def memefiles2tamo(files, tamoname):
global probefile, PROBESET, fsafile
motifs = []
for filename in files:
print ">>>SDFSD>F ",filename
if re.search('\.ace$',filename):
mdobject = AlignAce.AlignAce(filename)
if not mdobject.fastafile: mdobject.fastafile=filename.replace('.ace','.fsa')
elif re.search('\.meme.*$',filename):
mdobject = Meme.Meme(filename)
if not mdobject.fastafile:
mdobject.fastafile=re.sub('\..\.meme','.meme',filename).replace('.meme','.fsa')
motifs.extend(mdobject.motifs)
#fsaname = find_fsa(mdobject.fastafile)
print mdobject.fastafile
if fsafile: fsaname = fsafile
else: fsaname = Fasta.find(mdobject.fastafile)
fsaD = Fasta.load(fsaname)
probes = fsaD.keys()
if not probefile:
PROBESET = MotifMetrics.ProbeSet('YEAST')
#PROBESET= pick_genome(fsaname)
for key,seq in fsaD.items():
PROBESET.probes[key] = seq
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')
if motif.frac == None: motif.frac = PROBESET.frac(motif,probes,'v',0.7)
if re.search('\.meme$',filename):
motif.MAP = -math.log(motif.evalue)/math.log(10)
if 0 and (motif.CRA == None):
try:
pass
CRA, Cfrac = PROBESET.cons_ROC_AUC(motif,probes,'v',tuple='YES')
motif.CRA = CRA
motif.Cfrac = Cfrac
except: pass
if re.search('\.meme$',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(motifs,tamoname)
def main():
seqsD = Fasta.load(sys.argv[1])
seqs = seqsD.values()
for w in range(1,7):
allnmers = permute(w)
nmersT = MotifTools.top_nmers(w,seqs,'with counts','purge Ns')
nmersD = {}
total = 0
for nmer in allnmers:
nmersD[nmer] = 1 #Pseudo count
total = total + 1
for nmer,count in nmersT[:]:
try:
rc = MotifTools.revcomplement(nmer)
nmersD[nmer] = nmersD[nmer] + count
nmersD[rc] = nmersD[rc] + count
total = total + 2*count
except KeyError:
pass
_t = nmersD.keys()
_t.sort()
print "# freq in %s (total %d with pseudocounts)"%(sys.argv[1],total)
for nmer in _t:
print "%-7s %20.17f"%(nmer,float(nmersD[nmer]) / total)
sys.stdout.flush()
def loadMiRNAs(miRNA_Path):
"""
Takes fasta file of mature miRNAs.
Returns dict.
"""
return Fasta.load(miRNA_Path)
def info2seeds(N,infofile,probefile,species='YEAST'):
G = ProbeSet(species)
IDs = G.ids_from_file(probefile)
Q = EM.theMarkovBackground.zeroth()
seqs = Fasta.seqs(infofile)
if not N:
nmers = seqs
else:
nmers= MotifTools.top_nmers(N,seqs)
if len(nmers) > 1000: nmers = nmers[0:1000]
print "Scoring enrichment of %d nmers from %s"%len(nmers,infofile)
sys.stdout.flush()
nmers_scoresT = []
for nmer in nmers:
if nmer.isalpha():
p = G.p_value(nmer,IDs,'') #'verbose'
nmers_scoresT.append((nmer,p))
nmers_scoresT.sort(lambda x,y: cmp(x[1],y[1]))
last = min(20,len(nmers_scoresT))
models = []
for i in range(last):
seq = nmers_scoresT[i][0]
m = MotifTools.Motif('',Q)
m.compute_from_text(seq,0.1)
models.append(m)
for tup in nmers_scoresT[0:40]:
print tup
return(models)
def main(fastafile, outDirectory): # !! 1/2/09 AD added 'fastafile' var and changed 'if __name__' as way to call this from script.
seqsD = Fasta.load(fastafile)
seqs = seqsD.values()
output = []
for w in range(1,7):
allnmers = permute(w)
nmersT = MotifTools.top_nmers(w,seqs,'with counts','purge Ns')
nmersD = {}
total = 0
for nmer in allnmers:
nmersD[nmer] = 1 #Pseudo count
total = total + 1
for nmer,count in nmersT[:]:
try:
rc = MotifTools.revcomplement(nmer)
nmersD[nmer] = nmersD[nmer] + count
nmersD[rc] = nmersD[rc] + count
total = total + 2*count
except KeyError:
pass
_t = nmersD.keys()
_t.sort()
output.append("# freq in %s (total %d with pseudocounts)\n"%(fastafile.split('/')[-1],total)) # AD 02-27-09 added a '\n' to make file look right
for nmer in _t:
output.append( "%-7s %20.17f\n"%(nmer,float(nmersD[nmer]) / total)) # AD 02-27-09 added a '\n' to make file look right
# open output file and write out results
outFile = '%s/%s.freq' % (outDirectory, fastafile.split('/')[-1])
outFile = open(outFile, 'w')
for index in output:
outFile.write(index)
def main():
seqsD = Fasta.load(sys.argv[1])
seqs = seqsD.values()
for w in range(1, 7):
allnmers = permute(w)
nmersT = MotifTools.top_nmers(w, seqs, 'with counts', 'purge Ns')
nmersD = {}
total = 0
for nmer in allnmers:
nmersD[nmer] = 1 #Pseudo count
total = total + 1
for nmer, count in nmersT[:]:
try:
rc = MotifTools.revcomplement(nmer)
nmersD[nmer] = nmersD[nmer] + count
nmersD[rc] = nmersD[rc] + count
total = total + 2 * count
except KeyError:
pass
_t = nmersD.keys()
_t.sort()
print "# freq in %s (total %d with pseudocounts)" % (sys.argv[1],
total)
for nmer in _t:
print "%-7s %20.17f" % (nmer, float(nmersD[nmer]) / total)
sys.stdout.flush()
def calcStats(fastaPath):
seqFile = Fasta.load(fastaPath)
combinedSeq = ''
for each in seqFile:
combinedSeq += seqFile[each]
combinedSeq= combinedSeq.upper()
seqs = len(seqFile)
totNucs = len(combinedSeq)
aCnt = combinedSeq.count('A')
cCnt = combinedSeq.count('C')
gCnt = combinedSeq.count('G')
tCnt = combinedSeq.count('T')
nCnt = combinedSeq.count('N')
nonNs = aCnt+cCnt+gCnt+tCnt
n2tot = float(nCnt)/len(combinedSeq)
n2nonN = float(nCnt)/nonNs
percentGC = (float(gCnt)+cCnt)/nonNs
return {'seqLen':seqs,
'totNucs':totNucs,
'aCnt':aCnt,
'cCnt':cCnt,
'gCnt':gCnt,
'tCnt':tCnt,
'nCnt':nCnt,
'nonNs':nonNs,
'n2tot':n2tot,
'n2nonN':n2nonN,
'percentGC':percentGC}
def orf2pseq(orf):
global _orfpseqs
if not _orfpseqs:
from TAMO.seq import Fasta
_orfpseqs = Fasta.load(_ORFPSEQS)
for _orf, pseq in _orfpseqs.items():
if pseq[-1] == '*': _orfpseqs[_orf] = pseq[:-1]
if _orfpseqs.has_key(orf): return _orfpseqs[orf]
else: return ''
def orf2pseq(orf):
global _orfpseqs
if not _orfpseqs:
from TAMO.seq import Fasta
_orfpseqs = Fasta.load(_ORFPSEQS)
for _orf,pseq in _orfpseqs.items():
if pseq[-1] == '*': _orfpseqs[_orf] = pseq[:-1]
if _orfpseqs.has_key(orf): return _orfpseqs[orf]
else: return ''
def train_final(self, model, fg, bg, N, beta):
input_seqs = []
for s in fg:
iseq = self.all_probes[s].upper()
iseq = re.sub(";","",iseq)
if (re.search("N", iseq)):
iseq = re.sub("N","",iseq)
if (len(iseq)>0): input_seqs.append(iseq)
if (self.refine):
final_motif = self.train_model(model, input_seqs, beta)
else:
final_motif = self.models[model]
train_pos = self.get_LLRs(final_motif, fg)
train_neg = self.get_LLRs(final_motif, bg)
over_sampled_positive = self.SMOTE([train_pos], N, N)[0]
#Train SVM to classify our training set
c_vals = [1.0e-10, 1.0e-4, 1.0e-3, 1.0e-2, 0.05, 0.1, 1.0, 10.0, 100.0]
best_classifier = None
best_err = 1.0
for c in c_vals:
classifier = self.SVM_train(over_sampled_positive, train_neg, c)
train_err = self.SVM_test(classifier, over_sampled_positive, train_neg)
if (train_err<best_err):
best_err = train_err
best_classifier = classifier
(train_err, fp, fn) = self.SVM_test(best_classifier, train_pos, [], 1)
if (self.dump):
motif = {}
no_motif = {}
for name, val in zip(fg,train_pos):
train_err = self.SVM_test(best_classifier, [val], [])
if (train_err):
no_motif[name] = self.all_probes[name]
else:
motif[name] = self.all_probes[name]
motif_fsa = self.motif_file.split('.')[0] + '.pos.fsa'
no_motif_fsa = self.motif_file.split('.')[0] + '.neg.fsa'
Fasta.write(motif, motif_fsa)
Fasta.write(no_motif, no_motif_fsa)
return((final_motif, best_classifier, fn))
def loadSeqs(fastaPathList):
"""
Takes list of paths. Returns single dict full of seqs found in the files.
Converts softMasking to hard.
"""
rDict = {}
for path in fastaPathList:
rDict.update(Fasta.load(path))
bioDefs.softMaskDict2HardMask(rDict)
return rDict
def get_seq(chr,start=None,stop=None):
global ChrD
if not ChrD:
from TAMO.seq import Fasta
ChrD = Fasta.load(SGDdir + 'NCBI_yeast_genome.fsa')
if (type(chr) != type('')) or (chr.find('chr') != 0): # 1 -> chr1, 'X' -> chrX
chr = 'chr%s'%chr
if (start == None) and chr.find(':') > 0: # chr4:454-465 -> chr4, 454, 465
_chr,_range = chr.split(':')
chr = _chr
start, end = _range.split('-')
start, end = int(start), int(end)
return ChrD[chr][start-1:end]
def get_seq(chr, start=None, stop=None):
global ChrD
if not ChrD:
from TAMO.seq import Fasta
ChrD = Fasta.load(SGDdir + 'NCBI_yeast_genome.fsa')
if (type(chr) != type('')) or (chr.find('chr') !=
0): # 1 -> chr1, 'X' -> chrX
chr = 'chr%s' % chr
if (start == None) and chr.find(':') > 0: # chr4:454-465 -> chr4, 454, 465
_chr, _range = chr.split(':')
chr = _chr
start, end = _range.split('-')
start, end = int(start), int(end)
return ChrD[chr][start - 1:end]
def swp2swp(swp):
'Converts, when possible, from P014543 to ADR1_YEAST'
'Only works for yeast right now'
global _swp2swp
if not _swp2swp:
lines = Fasta.keys(_SWPFASTA, key_func=lambda x: x)
for line in lines:
toks = line.split()
text_name = toks[1]
numeric_name = toks[2]
if text_name[0:2] == 'SW' and numeric_name[0] == 'P':
_swp2swp[text_name[3:]] = numeric_name
_swp2swp[numeric_name] = text_name[3:]
if _swp2swp.has_key(swp):
return _swp2swp[swp]
def swp2swp(swp):
'Converts, when possible, from P014543 to ADR1_YEAST'
'Only works for yeast right now'
global _swp2swp
if not _swp2swp:
lines = Fasta.keys(_SWPFASTA,key_func=lambda x:x)
for line in lines:
toks = line.split()
text_name = toks[1]
numeric_name = toks[2]
if text_name[0:2] == 'SW' and numeric_name[0] == 'P':
_swp2swp[text_name[3:]] = numeric_name
_swp2swp[numeric_name] = text_name[3:]
if _swp2swp.has_key(swp):
return _swp2swp[swp]
def go(self):
"""Execution function: runs TAMO.MD.Meme.Meme and catches the output in self.output for access from MDAP."""
import time
# write a temp fasta file of coregulated seqs to use as input to Meme(file=TempFasta)
ctimeStr = time.ctime().replace(' ','_')
fileName = 'tempFastaOfCoRegSeqs.MDAP.%s.fas' %(ctimeStr)
tFasta = open(fileName, 'w')
tFastaTxt = Fasta.text(self.coRegSeqs[0])
tFasta.write(tFastaTxt)
# Call TAMO to do its thing:
self.output = Meme(file=fileName, width='', extra_args=self.extra_args, bfile=self.bfile)
# delete temp file
os.remove(fileName)
def LoadDNA(verbose=False):
###############################################################################
#
# Read DNA seqeuence
# Extract sub-sequence to model
# Define rules for DNA
#
###############################################################################
START_POS = 0
dna = ""
fastafile = params.GetString(DNA_section,"FILE")
if (fastafile):
chromo = params.GetString(DNA_section,"CHR")
chr_start = params.GetInt(DNA_section,"START")
chr_end = params.GetInt(DNA_section,"END")
if (not chr_end):
chr_end = params.GetInt(DNA_section,"LENGTH")
chr_end += chr_start
if verbose:
print ("Loading fasta: [%s]\n"%fastafile)
seqs = Fasta.load(fastafile)
seqkeys = seqs.keys()
seqkeys.sort()
n = 0
for chr in seqkeys:
n += len(seqs[chr])
if verbose:
print("Genome length = %d, # chromosomes = %d\n"%(n, len(seqkeys)))
if (seqs.has_key(chromo)):
seq = seqs[chromo]
if verbose:
print("Chr[%s] = %d nt\n"%(chromo,len(seq)))
dna = seq[chr_start:chr_end]
if verbose:
print("DNA[%d:%d] = %d nt\n"%(chr_start,chr_end,len(dna)))
else:
if verbose:
print("Cannot find [%s] chromosome in %s\n"%(chromo, filename))
if (verbose):
print("DNA:[%s]\n"%dna)
return dna
def spawnOrthoGroups(promoterFileList,nWayOrthoList):
"""Takes promoterFileList<listOfPaths> and nWayOrthoList<listOfLists> and spawns the orthoGroup
objects in a dictionary with keys = 'geneName1:geneName2:etc' that will be used to run the combined
hypergeometric analysis."""
# validation
assert type(promoterFileList) == type([]), \
'''promoterFileList must be a list of file paths.
You provided type: "%s"'''\
% (type(promoterFileList))
assert type(promoterFileList[0]) == type(''), \
'''promoterFileList must be a list of file paths.
promoterFileList[0] != type(''): "%s"'''\
% (type(promoterFileList[0]))
# load promoters
allPromoters = {}
for i in range(len(promoterFileList)):
oneGenome = Fasta.file2dict(promoterFileList[i])
for j in oneGenome:
allKeys = allPromoters.keys()
assert j not in allKeys, \
'''Detected duplicate gene name in promoterFileList! "%s"'''\
% (j)
allPromoters[j] = oneGenome[j]
# Build Groups
orthoGroups = {}
for i in range(len(nWayOrthoList)):
groupDict = {}
for j in range(len(nWayOrthoList[i])):
if allPromoters[nWayOrthoList[i][j]]:
groupDict[nWayOrthoList[i][j]] = allPromoters[nWayOrthoList[i][j]]
else:
break # we do not want orthoGroups that are missing members
if len(groupDict) != len(nWayOrthoList[i]):
break # we do not want orthoGroups that are missing members
else:
nWayOrthoList[i].sort()
orthoGroups[':'.join(nWayOrthoList[i])] = OrthoGroup(groupDict)
return orthoGroups
def main():
short_opts = 'f:'
long_opts = ['genome=', 'range=', 'top=', 'pcnt=', 'bgfile=']
try:
opts, args = getopt.getopt(sys.argv[1:], short_opts, long_opts)
except getopt.GetoptError:
print getopt.GetoptError.__dict__
usage()
if not opts: usage()
fastafile = ''
top_count = 10
top_pcnt = None
genome = 'YEAST'
w_start = 8
w_stop = 15
bgfile = MDSCAN_DIR + 'yeast_int.bg'
for opt, value in opts:
if opt == '-f': fastafile = value
if opt == '--genome': genome = value
if opt == '--top': top_count = int(value)
if opt == '--pcnt': top_pcnt = float(value)
if opt == '--range':
w_start, w_stop = [int(x) for x in value.split(',')]
print "#" + ' '.join(sys.argv)
probeids = Fasta.keys(fastafile)
Genome = MotifMetrics.ProbeSet(genome)
probeids = Genome.filter(probeids)
if top_pcnt:
top_count = max(top_count, int(top_pcnt / 100.0 * len(probeids)))
theMeta = metaMDscan(fastafile, w_start, w_stop, top_count)
for m in theMeta.motifs:
m.pvalue = Genome.p_value(m, probeids, 'v')
m.church = Genome.church(m, probeids, 'v')
sys.stdout.flush()
theMeta.motifs.sort(lambda x, y: cmp(x.pvalue, y.pvalue))
print_motifs(theMeta.motifs)
def LoadDNA():
###############################################################################
#
# Read DNA seqeuence
# Extract sub-sequence to model
# Define rules for DNA
#
###############################################################################
START_POS = 0
dna = ""
fastafile = params.GetString(DNA_section, "FILE")
if (fastafile):
chromo = params.GetString(DNA_section, "CHR")
chr_start = params.GetInt(DNA_section, "START")
chr_end = params.GetInt(DNA_section, "END")
if (not chr_end):
chr_end = params.GetInt(DNA_section, "LENGTH")
chr_end += chr_start
print("Loading fasta: [%s]\n" % fastafile)
seqs = Fasta.load(fastafile)
seqkeys = seqs.keys()
seqkeys.sort()
n = 0
for chr in seqkeys:
n += len(seqs[chr])
print("Genome length = %d, # chromosomes = %d\n" % (n, len(seqkeys)))
if (seqs.has_key(chromo)):
seq = seqs[chromo]
print("Chr[%s] = %d nt\n" % (chromo, len(seq)))
dna = seq[chr_start:chr_end]
print("DNA[%d:%d] = %d nt\n" % (chr_start, chr_end, len(dna)))
else:
print("Cannot find [%s] chromosome in %s\n" % (chromo, filename))
print("DNA:[%s]\n" % dna)
return dna
def swp_find_and_format(swp):
global _swp_seqs
if not _swp_seqs:
_swp_seqs = Fasta.load(_SWPFASTA,key_func=lambda x:x)
hits = []
for key in _swp_seqs.keys():
if key[0:60].find(swp) >= 0:
hits.append(key)
if not hits:
return None
if len(hits) > 1:
print "# Multiple matches found for %s:"%swp
for hit in hits: print '#',hit
return None
hit = hits[0]
seq = _swp_seqs[hit]
txt = ''
for i in range(0,len(seq),70):
txt = txt + seq[i:i+70] + '\n'
return txt
def go(self):
"""Execution function: coordinates options used and background GC calculation, then runs
TAMO.MD.AlignAce.MetaAce and catches the output in self.output for access from MDAP.
Output is TAMO.AligAce result object."""
import time
# Calc GC background of genomic sequences representing the
# entire data set if requested.
if self.mdapOptions['background'] == 1:
self.dataStats = seqStats.calcStats(self.mdapArgs[0])
self.gcback = self.dataStats['percentGC']
# write a temp fasta file of coregulated seqs to use as input to Meme(file=TempFasta)
ctimeStr = time.ctime().replace(' ','_')
fileName = 'tempFastaOfCoRegSeqs.MDAP.%s.fas' %(ctimeStr)
tFasta = open(fileName, 'w')
tFastaTxt = Fasta.text(self.coRegSeqs[0])
tFasta.write(tFastaTxt)
# call TAMO to do its thing
self.output = MetaAce(fileName, self.width, self.iterations, self.gcback)
pass
def swp_find_and_format(swp):
global _swp_seqs
if not _swp_seqs:
_swp_seqs = Fasta.load(_SWPFASTA, key_func=lambda x: x)
hits = []
for key in _swp_seqs.keys():
if key[0:60].find(swp) >= 0:
hits.append(key)
if not hits:
return None
if len(hits) > 1:
print "# Multiple matches found for %s:" % swp
for hit in hits:
print '#', hit
return None
hit = hits[0]
seq = _swp_seqs[hit]
txt = ''
for i in range(0, len(seq), 70):
txt = txt + seq[i:i + 70] + '\n'
return txt
def __init__(self,fastaSeqs, motifDict, thresh=0.5,window=200):
self.seqMaps = {}
# Get seqs from fasta
assert type(fastaSeqs) == type('string') or type(fastaSeqs) == type({}),\
'MapLib arg(fastaSeqs) must be string pointing to file or a seqDict.'
if type(fastaSeqs) == type('string'):
seqs = Fasta.load(fastaSeqs)
elif type(fastaSeqs) == type({}):
seqs = fastaSeqs
# Instantiate a SeqMap obj for each seq in seqs
c = 0
for k in seqs:
c += 1
assert c <= 250
realT1 = time()
self.seqMaps[k] = SeqMap(k, seqs[k], motifDict, thresh=thresh, window=window)
realT2 = time()
print '%.4f\t%s' % (realT2-realT1,c)
def main():
short_opts = 'f:'
long_opts = ['genome=', 'range=', 'top=', 'pcnt=', 'bgfile=']
try: opts, args = getopt.getopt(sys.argv[1:], short_opts, long_opts)
except getopt.GetoptError:
print getopt.GetoptError.__dict__
usage()
if not opts: usage()
fastafile = ''
top_count = 10
top_pcnt = None
genome = 'YEAST'
w_start = 8
w_stop = 15
bgfile = MDSCAN_DIR + 'yeast_int.bg'
for opt,value in opts:
if opt == '-f': fastafile = value
if opt == '--genome': genome = value
if opt == '--top': top_count = int(value)
if opt == '--pcnt': top_pcnt = float(value)
if opt == '--range': w_start,w_stop= [int(x) for x in value.split(',')]
print "#" + ' '.join(sys.argv)
probeids = Fasta.keys(fastafile)
Genome = MotifMetrics.ProbeSet(genome)
probeids = Genome.filter(probeids)
if top_pcnt: top_count = max(top_count,int(top_pcnt/100.0 * len(probeids)))
theMeta = metaMDscan(fastafile,w_start,w_stop,top_count)
for m in theMeta.motifs:
m.pvalue = Genome.p_value(m,probeids,'v')
m.church = Genome.church(m,probeids,'v')
sys.stdout.flush()
theMeta.motifs.sort(lambda x,y: cmp(x.pvalue,y.pvalue))
print_motifs(theMeta.motifs)
def geneList2FastaDict(geneList, sourceFastaPath, hardMasked=True):
"""
Returns a Dict of requested fasta recs in form SeqName:Sequence.
Defaults to HardMasked return seqeunces.
"""
sourceDict = Fasta.load(sourceFastaPath)
# make new dict of all genes both in geneList AND sourceDict
# new dict may be shorter than geneList!!!!!!
newDict = {}
for i in geneList:
if sourceDict[i]:
newDict[i] = sourceDict[i]
print "%s genes names given, %s found." % (len(geneList), len(newDict))
if hardMasked:
softMaskDict2HardMask(newDict)
return newDict
def __init__(self,species='YEAST',seqs=''):
if seqs:
self.sourcefile = 'Runtime (%d sequences)'%len(seqs)
elif species.find('.6MBG') >= 0:
self.sourcefile = species
elif species[0:5] == 'YEAST':
self.sourcefile = TAMO.paths.Whiteheaddir+'Yeast6kArray/yeast.intergenic.6.freq'
TAMO.paths.CHECK(self.sourcefile,'Whitehead')
elif species[0:5] == 'HUMAN':
self.sourcefile = TAMO.paths.Whiteheaddir+'Human13kArray/human_elongated_probbesQC250.6MBG'
TAMO.paths.CHECK(self.sourcefile,'Whitehead')
elif os.path.exists(re.sub('.fsa|.fasta','.6MBG',species)):
self.sourcefile = re.sub('.fsa|.fasta','.6MBG',species)
elif os.path.exists(species) and (species.find('.fsa') >=0):
self.sourcefile = species
print "EM.MarkovBackground: Computing background from %s"%species
sys.stdout.flush()
self.freqs_from_seqs(Fasta.seqs(species))
#elif os.path.exists(species):
# self.sourcefile = species
else:
print 'EM.MarkovBackground: Unknown species %s, using Yeast'
self.sourcefile = TAMO.paths.Whiteheaddir+'Yeast6kArray/yeast.intergenic.6.freq'
TAMO.paths.CHECK(self.sourcefile,'Whitehead')
self.species = species
self.D = {}
self.F = {} #Frequencies
self.CP = {} #log2(Conditional Probabilities) CP['ACTG'] = p( G | ACT )
self.nmers_by_size = map(lambda x:[],range(0,10))
self.highestorder = 0
if seqs:
print "EM.MarkovBackground: Computing background from %d sequences"%len(seqs)
self.freq_from_seqs(seqs)
else:
self.freq_from_file()
self.compute_conditional()
self.totD = {}
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))]]
from TAMO.seq import Fasta
from gusPyCode.defs import bioDefs
miRNAFile = '/Users/biggus/Documents/James/Data/Tu_miRNA/miRNAs/miRBase/mature.aga.fa'
seedFile = '/Users/biggus/Documents/James/Data/Tu_miRNA/miRNAs/miRBase/mature.aga.seeds.ctrl.fa'
oligoType = 'control' # 'match' or 'control'
assert oligoType == 'match' or 'control', 'oligoType MUST be only "match" or "control".'
# Load miRNA fastas into dict.
miRNAs = Fasta.file2dict(miRNAFile)
# Create new dict for seeds.
seeds = {}
# 1) Cycle through miRNA dict taking 7mers starting at pos 1
# and then pos2. Adapt key to reflect which.
# 2) Convert to all uppers and convert U's to T's
# 3) If oligoType == 'match', rvcmp each 7mer and adapt key
# to reflect which.
for miRNA in miRNAs:
pos1_seed = miRNAs[miRNA][:7].upper().replace('U','T')
pos2_seed = miRNAs[miRNA][1:8].upper().replace('U','T')
if oligoType == 'match':
seeds[miRNA+'_match_pos1'] = bioDefs.revComp(pos1_seed)
seeds[miRNA+'_match_pos2'] = bioDefs.revComp(pos2_seed)
else:
seeds[miRNA+'_ctrl_pos1'] = pos1_seed
seeds[miRNA+'_ctrl_pos2'] = pos2_seed
def freq_from_fasta(self,fastafile):
seqsD = Fasta.load(sys.argv[1])
seqs = seqsD.values()
self.freq_from_seqs(seqs)
from TAMO.MotifTools import top_nmers,Motif
from TAMO import MotifTools
from TAMO.seq import Fasta
from gusPyCode.defs.bioDefs import ifKmerInAll
seqFile = '/Users/biggus/Documents/James/Collaborations/Campbell/data/mainTwoGenes.fas'
outFile = '/Users/biggus/Documents/James/Collaborations/Campbell/data/mainTwoGenes.8mersInAll.txt'
kmerSize = 8
scoreThresh = 0.999999
seqs = Fasta.file2dict(seqFile)
# create new dict to store the seqs' kmers
seqsKmers = {}
for i in seqs:
seqsKmers[i] = top_nmers(kmerSize,[seqs[i]], purge_Ns = 1) # for some reason top_nmers fails silently if given str instead of list
inAllSeqs = []
count = 0
for seq in seqsKmers:
for kmer in seqsKmers[seq]:
if ifKmerInAll(kmer,seqs,scoreThresh):
if kmer not in inAllSeqs:
inAllSeqs.append(kmer)
count+=1
print count
outFile = open(outFile, 'w')
def main():
try:
opts, args = getopt.getopt(sys.argv[1:], "f:m:n:L:t:a:S:", ["help", "output="])
except getopt.GetoptError:
usage()
sys.exit(1)
if not opts:
usage()
sys.exit(1)
print "#" + ' '.join(sys.argv)
fastafile, motiffile, motifnums, labels, thresh = (None, None, [], None, 0.7)
ambigs = []
scale = 50.0 / 1000.0
motifs = []
for opt, value in opts:
#print opt, value
if opt == '-f': fastafile = value
elif opt == '-m': motifs.extend(MotifTools.txt2motifs(value))
elif opt == '-n': motifnums = [int(x) for x in value.split(',')]
elif opt == '-L': labels = list(value)
elif opt == '-t': thresh = float(value)
elif opt == '-a': ambigs.extend(value.split(','))
elif opt == '-S': scale = float(value)
probes = Fasta.load(fastafile)
if motiffile:
motifs.extend(TAMO.tamofile2motifs(motiffile))
if ambigs:
for ambig in ambigs:
motifs.append( MotifTools.Motif_from_text(ambig,0.1) )
if not motifnums: motifnums = range(len(motifs))
print '# %d: %s'%(len(motifs),motifnums)
for i in range(len(motifnums)):
motif = motifs[motifnums[i]]
if labels and i < len(labels):
txt = labels[i]
else:
txt = '%d'%i
print '%-3s : %s %5.2f (%4.2f)'%(txt,motif,thresh*motif.maxscore,thresh)
probehits = {}
for key in probes.keys():
hits_by_motif = []
save_flag = 0
if re.search('[BDHU]',probes[key]): continue
for num in motifnums:
result = motifs[num].scan(probes[key],thresh*motif.maxscore)
if result[0]:
hits_by_motif.append(result)
save_flag = 1
else:
hits_by_motif.append(None)
if save_flag:
probehits[key]=hits_by_motif
#scale = .1
maxw = 40
for key in probehits.keys():
l = len(probes[key])
a = list('-'* int(scale*l) )
a.extend( list(' '*10 ) )
desc = []
matches = probehits[key]
for i in range(len(matches)):
if matches[i]:
subseqs,endpoints,scores = matches[i]
for idx in range(len(subseqs)):
start,stop = endpoints[idx]
subseq = subseqs[idx]
score = scores[idx]
if labels and (i<len(labels)): ID = labels[i]
else : ID = '%d'%i
desc.append('%s %s %d-%d %4.2f '%(ID,subseq,start,stop,score))
start = int(start*scale)
for offset in range(10):
if a[start+offset] == '-':
if labels and (i < len(labels)):
a[start+offset] = labels[i]
else:
a[start+offset] = '%d'%i
break
print '%-14s %s'%(key,''.join(a)),
print ' '*max(0,maxw-len(a)), '| '.join(['%-27s'%x for x in desc])
print
print "Found matches in %d of %d input probes"%(len(probehits),len(probes))
def main():
if len(sys.argv) < 3:
print "Usage: %s <fasta_file> [width = None ] [options]" % (re.sub(
'^.*/', '', sys.argv[0]))
print "Options include:"
print " -valid <tf_name> Check answers against Transfac"
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 " -info <file.info> for structural priors"
print " -pad add NN..NN to seed"
print " "
print " Genome / Background model "
print " -human (250,1000) Use Human Background model"
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 = ''
cbeta = ''
deltamin = ''
gamma = 0.2
infofile = ''
seedmodels = []
species = 'YEAST'
valid_tfs = []
gapped_syl = None
gapflank = 0
gapweight = 0.2
enrichfact = 0.7
pmax = 0 #False
TFSEEDS = 0
TFMids = []
pad = None
padlen = 0
thetas = []
seed_count = 0 #Default: Take the top 0
seed_s = [] #Initialize seq array
sp_seed = 0
'''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 == 'sp': sp_seed = 1
elif tok == '-beta': beta = float(sys.argv[i + 1])
elif tok == '-beta': seedbeta = float(sys.argv[i + 1])
elif tok == '-cbeta': cbeta = float(sys.argv[i + 1])
elif tok == '-thetas':
for j in range(int(sys.argv[i + 1])):
thetas.append(float(sys.argv[i + j + 2]))
elif tok == '-gamma':
gamma = float(sys.argv[i + 1])
elif tok == '-delta':
deltamin = float(sys.argv[i + 1])
elif tok == '-info':
infofile = sys.argv[i + 1]
elif tok == '-valid':
valid_tfs.append(sys.argv[i + 1])
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 = sys.argv[i + 1]
padlen = sys.argv[i + 2]
elif tok == '-TF':
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
seqs = []
fsaD = Fasta.load(fastafile)
probes = fsaD.keys()
'''
for probeid in fsaD.keys():
seqs.append (fsaD [probeid])
'''
numprobes = len(probes)
#print "numprobes: %i"%numprobes
if not ('-random_background' in sys.argv or '-nomarkov' in sys.argv):
EM.loadMarkovBackground(fastafile, numprobes, species)
#seqs = EM.fasta2seqs(fastafile)
all_seqs = seqs
seed_s.extend(seqs[0:min(seed_count, len(seqs))])
#not necessary --- seed_c.extend(c_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:
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 = []
for m in seedmodels:
newmodels.append(m[-2, m.width + 2])
seedmodels = newmodels
'''
Set everything up and GO!!
'''
global theEM
theEM = EM.EM(seed_s, [], [], width, "VERBOSE")
if beta: theEM.beta = beta
if cbeta: theEM.cbeta = cbeta
if deltamin: theEM.deltamin = deltamin
if seedbeta: theEM.seedbeta = seedbeta
if thetas: theEM.thetas = thetas
theEM.param['gamma'] = gamma
theEM.probeids.extend(probes)
theEM.seqs.extend(all_seqs)
#theEM.cons_seqs.extend(c_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))
#sys.exit(0)
'''
Compute some metrics
'''
print "#Loading Genome %s" % species
sys.stdout.flush()
if species == 'human':
Genome = ProbeSet('HUMAN', enrichfact)
else:
Genome = ProbeSet(species, enrichfact)
ids = Genome.ids_from_file(fastafile)
#fsaDict = Fasta.load(fastafile)
#probes = fsaDict.keys()
#cons_pickle = fastafile.split('.')[0] + '.cpickle'
for C in theEM.candidates:
#p_cons = conservation_pvalue(C.pssm,probes,fsaDict,ConsDict,4)
#print p_cons
if not pmax:
w_dict = Genome.w_dict
for key, i in zip(w_dict.keys(), range(len(C.pssm.thetas))):
w_dict[key] = C.pssm.thetas[i]
Genome.w_dict = w_dict
C.pssm.pvalue = Genome.p_value(C.pssm, ids, 'verbose')
#print "P-VAL: %f"%(Genome.p_value(C.pssm,ids,'verbose')*p_cons)
C.pssm.church = Genome.church(C.pssm, ids)
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
for valid_tf in valid_tfs:
C.pssm.valid = Validate.validate(C.pssm, valid_tf, 'Verbose',
"Want Tuple")
'''
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:
_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
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 m.thetas != []:
tstr = "thetas:"
for theta in m.thetas:
tstr = tstr + " " + str(theta)
print tstr
#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
def info2seeds(N, infofile, probefile, species='YEAST'):
if species == 'human':
species = 'HUMAN'
G = ProbeSet(species)
IDs = G.ids_from_file(probefile)
Q = EM.theMarkovBackground.zeroth()
seqs = []
if re.search('.info$', infofile):
#I = infoana.Infofile(infofile,'DONT REMOVE QUERY')
I = infoana.Infofile(infofile)
print "# Loading infofile: %s" % infofile
print I
seqs = map(lambda x: 'NNNN%sNNNN' % x, I.bsites2seqs(50.0))
elif re.search('.fsa$', infofile):
fsaDict = Fasta.load(infofile)
probes = fsaDict.keys()
#sequence_repository = KenzieSequences()
cons_pickle = infofile.split('.')[0] + '.cpickle'
try:
CFH = open(cons_pickle, 'r')
ConsDict = pickle.load(CFH)
CFH.close()
except:
ConsDict = {}
for probe in probes:
seqs = []
cons = []
try:
seq_list = G.alignments[probe]
except:
continue
if (seq_list != []):
cer_seq = seq_list[0][1]
else:
cer_seq = ''
cer_seq = cer_seq.upper()
numg = len(seq_list) - 1
for i in range(1, 4):
try:
seqs.append(seq_list[i][1].upper())
except:
seqs.append('')
cons.append([])
for position in range(len(cer_seq)):
ref = cer_seq[position]
for i in range(3):
if (seqs[i] == ''): continue
if (seqs[i][position] != ref):
cons[i].append(1)
else:
cons[i].append(0)
ConsDict[probe] = cons
CFH = open(cons_pickle, 'w')
pickle.dump(ConsDict, CFH)
CFH.close()
for probe in probes:
superseq = ''
try:
seq_list = G.alignments[probe]
except:
continue
for seq in seq_list:
subseq = seq[1].replace('-', '')
subseq = subseq.replace('.', '')
seqs.append(subseq)
if not N:
nmers = seqs
else:
if (N < 11):
nmers = ConvergeMotifTools.top_nmers(N, seqs)
else:
gaplen = N - 2 * (N / 3)
gr = ''
for i in range(gaplen):
gr = gr + 'N'
nmers = ConvergeMotifTools.top_nmers(N, seqs, 0, '', 1)
gnmers = []
for nmer in nmers:
gnmers.append(nmer[0:(N / 3)] + gr + nmer[(N / 3):2 * (N / 3)])
nmers = gnmers
if len(nmers) > 201: nmers = nmers[0:200]
print "Scoring enrichment of %d nmers from .info file" % len(nmers)
nmers_scoresT = []
for nmer in nmers:
if nmer[0:(N / 3)].isalpha():
p = G.p_value(nmer, IDs, 'verbose')
#if (species=='Ciona'): ng = 2
#else: ng = 4
#p_cons = conservation_pvalue(nmer,IDs,fsaDict,ConsDict,ng)
#if (p_cons<0.1):
nmers_scoresT.append((nmer, p))
nmers_scoresT.sort(lambda x, y: cmp(x[1], y[1]))
#for tup in nmers_scoresT:
# print tup
last = min(20, len(nmers_scoresT))
models = []
for i in range(last):
seq = nmers_scoresT[i][0]
m = ConvergeMotifTools.Motif('', Q)
m.compute_from_text(seq, 0.1)
models.append(m)
return (models)
from TAMO.seq import Fasta fasFile = '/Users/biggus/Documents/James/Writings_Talks/Grants/09_Feb/PrelimData_Grant_Feb09/Clus2_247genes.fas' oFile1= '/Users/biggus/Documents/James/Writings_Talks/Grants/09_Feb/PrelimData_Grant_Feb09/Clus2_247genes.sample2.fas' oFile2= '/Users/biggus/Documents/James/Writings_Talks/Grants/09_Feb/PrelimData_Grant_Feb09/Clus2_247genes.test2.fas' firstDic, secDic = Fasta.random_split(fasFile,0.25) Fasta.write(firstDic,oFile1) Fasta.write(secDic,oFile2) print 'done'
from TAMO import MotifTools from TAMO.seq import Fasta from TAMO.MotifMetrics import ProbeSet from TAMO.MD.AlignAce import AlignAce from TAMO.MD.MDscan import MDscan from TAMO.MD.Meme import Meme #from TAMO.DataSources import GO from time import time fastaPath = '/Users/biggus/Documents/James/Data/ClusterDefs/TC-Fastas/TC-96.oneLine.fas' clusterIDS = Fasta.ids(fastaPath) totalSeqs = ProbeSet(fastaPath) # !! this is wrong should proly be goodAffys MDbg = '/Users/biggus/Documents/James/Data/2KB/2kb_Sequence/2kb_Anopheles/2KBupTSS_goodAffyAGAPsFastasOUT.masked.nr.MD.bg' outFile = '/Users/biggus/Documents/James/Data/ClusterDefs/testTAMOmetrics.txt' #theAce = AlignAce(fastaPath,width=10) print 'running MDscan...' tMD_1 = time() MDmotifs = MDscan(fastaPath) #,bgfile=MDbg) tMD_2 = time() MD_time = tMD_2-tMD_1 print 'MDscan took %.5f sec == %.3f min.\nMDscan found %s motifs.' % (MD_time,MD_time/60.0, len(MDmotifs.motifs)) print 'running MEME...' tMeme_1 = time() memeMotifs = Meme(fastaPath) tMeme_2 = time() Meme_time = tMeme_2-tMeme_1
def motif_matrix(fsa, motif, outfile, genome='mm9'):
if genome == 'hg18':
markov = "/nfs/genomes/human_gp_mar_06/hg18_promoters_3000_1000.markov"
else:
markov = "/nfs/data/cwng/chipseq/hypotheses/Mouse.markov"
#Load motif and background adjust PSSM
m = MotifTools.load(motif)
EM.loadMarkovBackground(markov)
bg = EM.theMarkovBackground.zeroth()
F = Fasta.load(fsa, key_func=lambda x: x)
seqs = F.values()
n_seqs = len(seqs)
n_motifs = len(m)
SCORES = np.zeros((n_motifs, n_seqs), dtype='float')
#SHIFTS=np.zeros((n_motifs,n_seqs))
#out=open(outfile,'w')
for i, M in enumerate(m):
ll = M.logP
EM.loadMarkovBackground(markov)
bg = EM.theMarkovBackground.zeroth()
for pos in ll:
for letter in pos.keys():
pos[letter] = pos[letter] - math.log(
bg[letter]) / math.log(2.0)
AM = MotifTools.Motif_from_ll(ll)
#adj_model = MotifTools.Motif_from_ll(ll)
#adj_model.source = M.source
#pssm = MDsupport.Motif2c_PSSM(adj_model)
#w=pssm.width
#shift=[]
#scores=[]
mi, ma = AM.minscore, AM.maxscore
#F_m={}
#Search every seq for given motif above threshold t and print motif centered results
for j, seq in enumerate(seqs):
seq_fwd = seq.upper()
#seq_rev = str(MotifTools.revcomplement(seq_fwd))[::-1]
#scores_fwd = pssm.score_probe(seq_fwd)
#scores_rev = pssm.score_probe(seq_rev)
#max_score=mi
#max_ind=0
#for ind,s in enumerate(scores_fwd):
# if s> max_score:
# max_score=s
# max_ind=ind
# strand='+'
#for ind,s in enumerate(scores_rev):
# if s> max_score:
# max_score=s
# max_ind=ind
# strand='-'
max_score = AM.bestscore(seq_fwd)
mscore = (max_score - mi) / (ma - mi)
#orig=len(seq_fwd)/2
#bind=max_ind+w//2
#d=abs(orig-bind)
SCORES[i, j] = mscore
#SHIFTS[i,j]=d
#out.write('%1.3f\t'%mscore)
#out.write('\n')
#out.close()
#del F
np.savetxt(outfile, SCORES, fmt='%1.3f')
from TAMO.seq import Fasta
from gusPyCode.defs.bioDefs import geneList2FastaDict
from gusPyCode.defs.mosqData import promoterSeqPaths
geneList = map(lambda l: l.strip(), \
open('/Users/biggus/Documents/James/Collaborations/Campbell/data/CCupAt4Days.gte2x.genes.txt', 'rU'))
sourceFasta = promoterSeqPaths.Aa_2000bpUp_hardMasked_shuf1
oFile = '/Users/biggus/Documents/James/Collaborations/Campbell/data/CCupAt4Days.gte2x.masked.shuffled.1.fas'
newFasta = geneList2FastaDict(geneList, sourceFasta, hardMasked=True)
newFasta = Fasta.text(newFasta)
oFile = open(oFile, 'w')
oFile.write(newFasta)
print 'Done'
def memefiles2tamo(files, tamoname):
global probefile, PROBESET
motifs = []
for filename in files:
print ">>>SDFSD>F ", filename
if re.search('\.ace$', filename):
mdobject = AlignAce.AlignAce(filename)
if not mdobject.fastafile:
mdobject.fastafile = filename.replace('.ace', '.fsa')
elif re.search('\.meme.*$', filename):
mdobject = Meme.Meme(filename)
if not mdobject.fastafile:
mdobject.fastafile = re.sub('\..\.meme', '.meme',
filename).replace('.meme', '.fsa')
motifs.extend(mdobject.motifs)
#fsaname = find_fsa(mdobject.fastafile)
print mdobject.fastafile
fsaname = Fasta.find(mdobject.fastafile)
fsaD = Fasta.load(fsaname)
probes = fsaD.keys()
if not probefile:
PROBESET = MotifMetrics.ProbeSet('YEAST')
#PROBESET= pick_genome(fsaname)
for key, seq in fsaD.items():
PROBESET.probes[key] = seq
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')
if motif.frac == None:
motif.frac = PROBESET.frac(motif, probes, 'v', 0.7)
if re.search('\.meme$', filename):
motif.MAP = -math.log(motif.evalue) / math.log(10)
if 1 and (motif.CRA == None):
try:
pass
CRA, Cfrac = PROBESET.cons_ROC_AUC(motif,
probes,
'v',
tuple='YES')
motif.CRA = CRA
motif.Cfrac = Cfrac
except:
pass
if re.search('\.meme$', 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(motifs, tamoname)
def main():
try:
opts, args = getopt.getopt(sys.argv[1:], "f:m:n:L:t:a:S:i:", ["help", "output="]) # AD added 'i'
except getopt.GetoptError:
usage()
sys.exit(1)
if not opts:
usage()
sys.exit(1)
print "#" + ' '.join(sys.argv)
fastafile, motiffile, motifnums, labels, thresh = (None, None, [], None, 0.75) # AD changed thresh val to 0.75 from 0.7
ambigs = []
scale = 50.0 / 1000.0
motifs = []
for opt, value in opts:
#print opt, value
if opt == '-f': fastafile = value
elif opt == '-m': motifs.extend(MotifTools.txt2motifs(value))
elif opt == '-n': motifnums = [int(x) for x in value.split(',')]
elif opt == '-L': labels = list(value)
elif opt == '-t': thresh = float(value)
elif opt == '-a': ambigs.extend(value.split(','))
elif opt == '-S': scale = float(value)
elif opt == '-i': motiffile = value # AD added this option to ACTUALLY supply the tamo motif file at the command-line. The code to deal with motiffiles already existed. There was just no code for User to supply one.
probes = Fasta.load(fastafile)
if motiffile:
for f in motiffile.split(','): # AD added this to allow supplying multiple tamo files at the prompt like you can supply multiple motifs
motifs.extend(MotifTools.load(f))
if ambigs:
for ambig in ambigs:
motifs.append( MotifTools.Motif_from_text(ambig,0.1) )
if not motifnums: motifnums = range(len(motifs))
print '# %d: %s'%(len(motifs),motifnums)
for i in range(len(motifnums)):
motif = motifs[motifnums[i]]
if labels and i < len(labels):
txt = labels[i]
else:
txt = '%d'%i
print '%-3s : %s %5.2f (%4.2f)'%(txt,motif,thresh*motif.maxscore,thresh)
probehits = {}
for key in probes.keys():
hits_by_motif = []
save_flag = 0
if re.search('[BDHU]',probes[key]): continue
for num in motifnums:
result = motifs[num].scan(probes[key],thresh*motif.maxscore)
if result[0]:
hits_by_motif.append(result)
save_flag = 1
else:
hits_by_motif.append(None)
if save_flag:
probehits[key]=hits_by_motif
#scale = .1
maxw = 40
for key in probehits.keys():
l = len(probes[key])
a = list('-'* int(scale*l) )
a.extend( list(' '*10 ) )
desc = []
matches = probehits[key]
for i in range(len(matches)):
if matches[i]:
subseqs,endpoints,scores = matches[i]
for idx in range(len(subseqs)):
start,stop = endpoints[idx]
subseq = subseqs[idx]
score = scores[idx]
if labels and (i<len(labels)): ID = labels[i]
else : ID = '%d'%i
desc.append('%s %s %d-%d %4.2f '%(ID,subseq,start,stop,score))
start = int(start*scale)
for offset in range(10):
if a[start+offset] == '-':
if labels and (i < len(labels)):
a[start+offset] = labels[i]
else:
a[start+offset] = '%d'%i
break
print '%-14s %s'%(key,''.join(a)),
print ' '*max(0,maxw-len(a)), '| '.join(['%-27s'%x for x in desc])
print
print "Found matches in %d of %d input probes"%(len(probehits),len(probes))
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