def search(self,dnaSeq,pValThresh=0.05,halfAT=0.25,halfGC=0.25):
"""Return motility hits tuple."""
def _setThresh(newPval):
"""Return threshold that is approximately
exquivilent to a p-value of 'pValThresh'"""
maxScore = mot.max_score()
minScore = mot.min_score() # to be used for lower bound (but later.. :( )
scoreSteps = [maxScore*(x/100.0) for x in range(1,101)]
scoreSteps.reverse()
thresh = maxScore
for score in scoreSteps:
pVal = mot.weight_sites_over(score,AT_bias=halfAT, GC_bias=halfGC)
print 'score: %s pVal: %s ' % (score,pVal)
if pVal <= newPval:
thresh = score
elif pVal > newPval:
break
if pVal <= newPval:
return thresh
else:
return None
if not self._motility:
self._motility = motility.make_pwm(self._getMotilityMatrix())
self._motility.threshPval = pValThresh
self._motility.threshScore = _setThresh(pValThresh)
if not self._motility.threshPval == pValThresh:
self._motility.threshScore = _setThresh()
def test_5():
"""
Test calc_score & calc_energy equivalence
"""
motif = 'ACGG'
pwm = motility.make_pwm([motif])
operator = motility.make_operator([motif])
print pwm.calc_score(motif)
print operator.calc_score(motif)
print operator.calc_energy(motif)
assert operator.calc_score(motif) == operator.calc_energy(motif)
def test_5():
"""
Test calc_score & calc_energy equivalence
"""
motif = 'ACGG'
pwm = motility.make_pwm([motif])
operator = motility.make_operator([motif])
print pwm.calc_score(motif)
print operator.calc_score(motif)
print operator.calc_energy(motif)
assert operator.calc_score(motif) == operator.calc_energy(motif)
def test_4():
"""
Test misc coord handling / match str extraction
"""
motif = 'ACGG'
pwm = motility.make_pwm([motif])
pwm_match = pwm.find(motif, 4)
iupac_match = motility.find_iupac(motif, motif)
exact_match = motility.find_exact(motif, motif)
assert pwm_match == iupac_match
assert pwm_match == exact_match
rcmotif = 'CCGT'
pwm_match = pwm.find(rcmotif, 4)
iupac_match = motility.find_iupac(rcmotif, motif)
exact_match = motility.find_exact(rcmotif, motif)
assert pwm_match == iupac_match
def test_4():
"""
Test misc coord handling / match str extraction
"""
motif = 'ACGG'
pwm = motility.make_pwm([motif])
pwm_match = pwm.find(motif, 4)
iupac_match = motility.find_iupac(motif, motif)
exact_match = motility.find_exact(motif, motif)
assert pwm_match == iupac_match
assert pwm_match == exact_match
rcmotif = 'CCGT'
pwm_match = pwm.find(rcmotif, 4)
iupac_match = motility.find_iupac(rcmotif, motif)
exact_match = motility.find_exact(rcmotif, motif)
assert pwm_match == iupac_match
def get_consensus_snv_arrary(
dic_chr_pos_snv, chr, snv_pos_list, snv_arrary_list
): # get consensus SNV (also called 'phasing') using Position-Weight Matrices (commonly used in DNA/RNA/Protein motif finding)
# first step: filter some SNV positions with many missing data
retain_snv_idx = []
retain_snv_pos_list = []
for i in range(len(snv_pos_list)):
ret_snv_count = 0
for snv_arrary in snv_arrary_list:
if snv_arrary[i] != "-":
ret_snv_count += 1
if float(ret_snv_count) / len(
snv_arrary_list
) >= 0.5: # require >50% long read have nucleotide
retain_snv_idx.append(i)
retain_snv_pos_list.append(str(snv_pos_list[i]))
retain_snv_pos = ",".join(retain_snv_pos_list)
# second step: get the consensus (phased major allele)
if retain_snv_idx != []: # have valid SNV position for constructing new SNV arrary
# obtain new SNV arrary
new_snv_arrary_list = []
for snv_arrary in snv_arrary_list:
new_snv_arrary = ""
for idx in retain_snv_idx:
new_snv_arrary += snv_arrary[idx]
new_snv_arrary_list.append(new_snv_arrary)
# obtain SNV arrary for constructing consensus
consus_snv_arrary_list = []
for new_snv_arrary in new_snv_arrary_list:
if "-" not in new_snv_arrary:
consus_snv_arrary_list.append(new_snv_arrary)
if consus_snv_arrary_list != []: # have valid SNV arrary for constructing consensus
# get major allele by most common element in the list
counter_cm_ele_dic = collections.Counter(consus_snv_arrary_list)
most_cm_ele = counter_cm_ele_dic.most_common(2)
if most_cm_ele[0][
1] >= 5: # if the snv_arrary is support by >= 5 long reads
major_allele = most_cm_ele[0][0]
else:
# get major allele by PWM
pwm = motility.make_pwm(
consus_snv_arrary_list) # PWM calculate
dic_snv_uniq_score = {}
for snv_uniq in set(consus_snv_arrary_list):
snv_score = float(pwm.calc_score(snv_uniq))
if snv_score not in dic_snv_uniq_score.keys():
dic_snv_uniq_score[snv_score] = []
dic_snv_uniq_score[snv_score].append(snv_uniq)
else:
dic_snv_uniq_score[snv_score].append(snv_uniq)
max_consus_snv = dic_snv_uniq_score[max(
dic_snv_uniq_score.keys()
)][0] # get SNV arrary with maximal PWM score as major allele
major_allele = max_consus_snv
# get minor allele
minor_phase_list = []
for i in range(len(retain_snv_pos_list)):
if dic_chr_pos_snv[chr][int(
retain_snv_pos_list[i])][0] != major_allele[i]:
minor_phase_list.append(dic_chr_pos_snv[chr][int(
retain_snv_pos_list[i])][0])
else:
minor_phase_list.append(dic_chr_pos_snv[chr][int(
retain_snv_pos_list[i])][1])
minor_allele = "".join(minor_phase_list)
# quantify allele-specific read count for both alleles
major_allele_c, minor_allele_c, uncertain_allele_c = 0, 0, 0
for new_snv_arrary in new_snv_arrary_list:
common_count_major = sum(
1 for a, b in zip(major_allele, new_snv_arrary) if a == b)
common_count_minor = sum(
1 for a, b in zip(minor_allele, new_snv_arrary) if a == b)
if float(common_count_major) / len(major_allele) > 0.5:
major_allele_c += 1
elif float(common_count_minor) / len(minor_allele) > 0.5:
minor_allele_c += 1
else:
uncertain_allele_c += 1
output_res = "\t".join([
retain_snv_pos, major_allele, minor_allele,
str(major_allele_c),
str(minor_allele_c),
str(uncertain_allele_c)
])
else:
output_res = "\t".join(["*", "*", "*", "*", "*", "*"])
else:
output_res = "\t".join(["*", "*", "*", "*", "*", "*"])
return output_res
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,)
targetGenes = "/Users/biggus/Documents/James/Data/ReClustering/kmedsPear33Clus50x_2/Clus2_247genes.genes.txt"
targetGenes = map(lambda l: l.strip(), open(targetGenes, "rU"))
targetGenes = targetGenes[:genes]
for i in range(len(targetGenes)):
targetGenes[i] = seqs[targetGenes[i]]
motifs = []
tMotifs = []
mMotifs = []
for t in tmoFiles:
Ms = loadTMOs(t)
motifs.extend(Ms)
for i in range(len(motifs)):
tMotifs.append(Motif(motifs[i].bogus_kmers()))
mMotifs.append(motility.make_pwm(motifs[i].bogus_kmers()))
tTimeIt = """for m in tMotifs:
for s in targetGenes:
m.scan(s,factor=0.75)
"""
mTimeIt = """for m in mMotifs:
for s in targetGenes:
m.find(s,m.max_score()*0.85)
"""
tTimer = timeit.Timer(tTimeIt, "from __main__ import tMotifs,targetGenes")
mTimer = timeit.Timer(mTimeIt, "from __main__ import mMotifs,targetGenes")