def _parse(self):
i = 0
while i < len(self.lines):
line = self.lines[i]
toks = line.split()
if '-i' in toks and not self.fastafile:
idx = toks.index('-i')
self.fastafile = toks[idx+1]
if (len(toks) > 0 and toks[0] == 'Motif'):
seqs = []
while 1:
i = i + 1
line = self.lines[i]
toks = line.split()
if toks[0][0] == '*': break
seqs.append(toks[0])
M = Motif(seqs)
i = i + 1
line = self.lines[i]
toks = line.split()
MAP = float(toks[2])
if MAP > 1000: MAP = 0 #likely to be an AlignACE error
M.MAP = MAP
self.motifs.append(M)
i = i + 1
self.nmotifs = len(self.motifs)
def generateMotifs(seqGroups, align, outputPrefix, transSeq=False,
extendAlphabet=False, clusterMotifs=False, protein=False, threads=2, stream=None):
from TAMO.MotifTools import Motif
ighvMotifs = []
if clusterMotifs and 'gene' in outputPrefix:
findMotifClusters(ighvMotifs, outputPrefix, stream=stream)
printto(stream, '\t\tPWMs, consensus and logos are being generated for {} motifs ... '.format(len(seqGroups)))
pwmFile = open(outputPrefix + '_pwm.txt', 'w')
consensusFile = open(outputPrefix + '_consensus.txt', 'w')
logosFolder = outputPrefix + '_logos'
if not os.path.exists(logosFolder):
os.makedirs(logosFolder)
# create the sequence alphabet: DNA or Protein
alphabet = createAlphabet(align, transSeq, extendAlphabet, protein)
groups = seqGroups.keys()
groups.sort()
for group in groups:
filename = os.path.join(logosFolder, group.replace('/', '') + '.png')
seqs = seqGroups[group]
m = generateMotif(seqs, group, alphabet, filename, align, transSeq, protein, outDir=logosFolder,
threads=threads, stream=stream)
if m is None:
# motif file found, no further work required
return
motifSeqs = m.instances
pwm = m.counts.normalize(pseudocounts=None) # {'A':0.6, 'C': 0.4, 'G': 0.4, 'T': 0.6}
consensusMax = str(m.consensus)
pwmFile.write('#{} {} sequences\n'.format(group, len(motifSeqs)))
pwmFile.write(str(pwm))
consensusFile.write('>{} max_count\n'.format(group))
consensusFile.write(consensusMax + '\n')
# print(str(m.anticonsensus)) # smallest values in the columns
if not transSeq and not align and not protein:
consensusIupac = str(m.degenerate_consensus)
# print(consensusIupac) # IUPAC ambiguous nucleotides
consensusFile.write('>{} degenerate\n'.format(group))
consensusFile.write(consensusIupac + '\n')
pwmFile.flush()
consensusFile.flush()
gc.collect()
if clusterMotifs and len(motifSeqs) > 10:
motif = Motif(map(lambda x: str(x), motifSeqs),
backgroundD={'A': 0.6, 'C': 0.4, 'G': 0.4, 'T': 0.6}, id=group)
motif.addpseudocounts(0.1)
ighvMotifs.append(motif)
pwmFile.close()
consensusFile.close()
gc.collect()
printto(stream, "\tPosition weight matrices are written to " + os.path.basename(outputPrefix + '_pwm.txt'))
printto(stream, "\tConsensus sequences are written to " + os.path.basename(outputPrefix + '_consensus.txt'))
if clusterMotifs:
findMotifClusters(ighvMotifs, outputPrefix, stream=stream)
def ifKmerInAll(kmer,dictOfSeqs, factor=1):
kmer = Motif(kmer)
results = []
for seq in dictOfSeqs:
temp = kmer.scan(dictOfSeqs[seq], factor=factor)
if temp[0]:
results.append(True)
else:
results.append(False)
if False in results:
return False
else:
return True
def taMotif2MopatMatrix(taMotif):
"""
Uses ONE tamo motif to produce a probability matrix compatible with MOPAT.
RETURNS: [oneLetterName, [list of lists]]
"""
name = taMotif.oneletter.replace('.','n')
matrix = []
# Use logLiklihoods to generate info for motif.counts
taMotif = Motif(taMotif.bogus_kmers())
for pos in taMotif.counts:
mopatPos = []
for nuc in sorted(pos.keys()):
mopatPos.append(pos[nuc])
matrix.append(mopatPos)
return [name, matrix]
def _parse(self):
'Parse MDscan file'
alloutput = '\n'.join(self.lines)
premotifs = alloutput.split('\nMtf ')
print len(premotifs)
for pm in premotifs:
sublines = pm.split('\n')
score, seednum = 0, 0
seqs = []
for line in sublines:
if line.find('Final Motif') == 0:
toks = line.split()
score = float(toks[6])
seednum = int(toks[8])
if line.find('>') == 0:
seqs.append(line.split()[-1])
#print "SEQS: ",seqs
if seqs:
m = Motif(seqs)
m.MAP = score
m.seednum = seednum
self.motifs.append(m)
def _parse(self):
'Parse MDscan file'
alloutput = '\n'.join(self.lines)
premotifs = alloutput.split('\nMtf ')
print len(premotifs)
for pm in premotifs:
sublines = pm.split('\n')
score, seednum = 0,0
seqs = []
for line in sublines:
if line.find('Final Motif') == 0:
toks = line.split()
score = float(toks[6])
seednum = int(toks[8])
if line.find('>') == 0:
seqs.append(line.split()[-1])
#print "SEQS: ",seqs
if seqs:
m = Motif(seqs)
m.MAP = score
m.seednum = seednum
self.motifs.append(m)
from TAMO.MotifTools import Motif
import motility
tM = Motif('WGATAR')
sites = tM.bogus_kmers()
tM = Motif(sites)
mM = motility.make_pwm(sites)
s = 'ATGCATGCTAGCGGCTGATAACGCTTATCATATGC'
mReults = mM.find(s,mM.max_score()*0.75,)
