def create_COG(mode, mot):
# generate random sequence set1 (100 seqs of length 300 bp)
num_seqs_in_set = 100
len_seq = 300
geom_rvs = geom.rvs(0.75, size=num_seqs_in_set,
loc=-1) #sym2=0.75, sym3=.7. Originally 0.5
set1 = MGlib.random_DNA(len_seq, {
'A': 0.3,
'C': 0.2,
'G': 0.2,
'T': 0.3
}, num_seqs_in_set)
# sample large number of sites from motif
pmot1 = MGlib.sample_motif(mot, num_seqs_in_set)
if mode == "positive":
#insert sites in sequences
e = 0
while (e < len(set1)):
# edit sequence to include random site(s)
# determine number of sites per geometric distribution
num_sites = geom_rvs[e]
new_sites = ""
for j in range(0, num_sites):
new_sites += random.choice(pmot1)
if len(new_sites) > len_seq:
new_sites = new_sites[:len_seq]
set1[e] = new_sites + set1[e][len(new_sites):]
e = e + 1
set2 = set1
return set2
def create_COG(mode, mot):
# generate random sequence set1 (100 seqs of length 300 bp)
num_seqs_in_set = 100
len_seq = 300
geom_rvs = geom.rvs(0.75, size=num_seqs_in_set, loc=-1) # sym2=0.75, sym3=.7. Originally 0.5
set1 = MGlib.random_DNA(len_seq, {"A": 0.3, "C": 0.2, "G": 0.2, "T": 0.3}, num_seqs_in_set)
# sample large number of sites from motif
pmot1 = MGlib.sample_motif(mot, num_seqs_in_set)
if mode == "positive":
# insert sites in sequences
e = 0
while e < len(set1):
# edit sequence to include random site(s)
# determine number of sites per geometric distribution
num_sites = geom_rvs[e]
new_sites = ""
for j in range(0, num_sites):
new_sites += random.choice(pmot1)
if len(new_sites) > len_seq:
new_sites = new_sites[:len_seq]
set1[e] = new_sites + set1[e][len(new_sites) :]
e = e + 1
set2 = set1
return set2
for cnt in range(0, 100):
print "Experiment: ", cnt
#create background sequence set: 100 seqs 283 bp long
set1 = MGlib.random_DNA(283, {'A': 0.3, 'C': 0.2, 'G': 0.2, 'T': 0.3}, 100)
set2 = set1[:]
#compute softmax scores for background sequences in dataset
gscr = MGlib.esfmax_score_seqs(set1, pssm, rpssm)
#compute softmax scores for motif sequences
mscr = MGlib.esfmax_score_seqs(mot.instances, pssm, rpssm)
#get normal distributions for background and motif
n_g = norm(mean(gscr), std(gscr))
n_m = norm(mean(mscr), std(mscr))
#create motif instances
pmot1 = MGlib.sample_motif(mot, 100)
pmot2 = MGlib.sample_motif(mot, 1)
#insert sites in sequences
e = 0
while (e < len(set1)):
#insert random site in first 10 sequences
if (e < 11):
set1[e] = random.choice(pmot1) + set1[e]
set2[e] = pmot2[0] + set2[e]
#otherwise insert random sequence from own sequence start
else:
set1[e] = set1[e][:17] + set1[e]
set2[e] = set2[e][:17] + set2[e]
e = e + 1
#loop experiments
for cnt in range(0,100):
print "Experiment: ", cnt
#create background sequence set: 100 seqs 283 bp long
set1 = MGlib.random_DNA(283,{'A': 0.3,'C': 0.2,'G': 0.2,'T': 0.3},100)
#compute softmax scores for background sequences in dataset
gscr = MGlib.esfmax_score_seqs(set1,pssm,rpssm)
#compute softmax scores for motif sequences
mscr = MGlib.esfmax_score_seqs(mot.instances,pssm,rpssm)
#get normal distributions for background and motif
n_g=norm(mean(gscr), std(gscr))
n_m=norm(mean(mscr), std(mscr))
#create motif instances
pmot1 = MGlib.sample_motif(mot,100)
#insert sites in sequences
e=0
while (e<len(set1)):
set1[e] = pmot1[e] + set1[e]
e = e+1
#compute softmax scores for sequences in dataset
scrs=MGlib.esfmax_score_seqs(set1,pssm,rpssm)
#get log-likelihoods for sequences in dataset
llrs=MGlib.ll_ratios(scrs,n_g,n_m,alpha)
#get per-sequence posterior for the sequences in dataset
fposts=MGlib.PostP(llrs,PPR,1)
print "Experiment: ", cnt
#in each experiment, create dataset with 100 seqs, sz with sites inserted
for cnt2 in range(0,1):
#create background sequence set: 100 seqs 283 bp long
set1 = MGlib.random_DNA(283,{'A': 0.3,'C': 0.2,'G': 0.2,'T': 0.3},100)
#compute softmax scores for background sequences in dataset
gscr = MGlib.esfmax_score_seqs(set1,pssm,rpssm)
#compute softmax scores for motif sequences
mscr = MGlib.esfmax_score_seqs(mot.instances,pssm,rpssm)
#get normal distributions for background and motif
n_g=norm(mean(gscr), std(gscr))
n_m=norm(mean(mscr), std(mscr))
#create motif instances
pmot1 = MGlib.sample_motif(mot,250)
#determine dataset size
if (cnt2==0):
sz=12
#insert sites in sequences
e=0
while (e<len(set1)):
#insert random site in first 10 sequences
if (e<sz+1):
set1[e] = random.choice(pmot1) + set1[e]
#otherwise insert random sequence from own sequence start
else :
set1[e] = set1[e][:17] + set1[e]
e = e+1
def main():
"""Gets a motif from file and reads it. It then generates synthetic data
to represent a set of promoters (100) mapping to a particular eggNOG/COG,
inserts into these sequences pseudosites (generated from the
distribution implicit in the PSSM). It then calls the PSSM evaluation
function to score the sites using the softmax function and then the
different functions to compute the likelihoods and the posterior
probabilities.
Usage:
MG_synth -M <Motif file> -O <out file prefix> -E <experiment> \
-A <alpha mix ratio> -P <Regulation prior> -T <theta> \
-V <verbose mode>
Note: motifs are assumed to be in FASTA or 1-per-line text format
"""
#set default parameters
motif_filename="CsoR.txt" #input file
out_filename="_o" #o prefix for output
verbose=0 #verbose mode
alpha=1.0/300.0 #mixing ratio for regulated model
rproms=3.0 #number of regulated promoters [prior]
tproms=1811.0 #total number of promoters in genome [prior]
experiment=2 #the experiment number
#get cmd parameters
try:
opts, args=getopt.getopt(sys.argv[1:],"I:O:V")
except getopt.GetoptError:
print 'MG_synth -M <Motif file> -O <out file prefix> -E <experiment> \
-A <alpha mix ratio> -P <Regulation prior> -T <theta> \
-V <verbose mode>'
sys.exit(2)
#assign parameters
for opt, arg in opts:
if opt == '-M':
motif_filename=arg
elif opt == '-O':
out_filename=arg
elif opt == '-E':
experiment=int(arg)
elif opt == '-A':
alpha=float(arg)
elif opt == '-P':
PR=float(arg)
elif opt == '-T':
theta=float(arg)
elif opt == '-V':
verbose=arg
elif opt == '-askme':
motif_filename = raw_input('Enter the motif file name\n')
out_filename = raw_input('Enter the output file name prefix\n')
experiment = raw_input('Enter the experiment number\n')
alpha = raw_input('Enter the alpha mixing ratio\n')
PR = raw_input('Enter the prior probability for regulation\n')
theta = raw_input('Enter the theta sensitivity threshold\n')
verbose = raw_input('Enter verbose mode (1/0)\n')
out_filename=motif_filename.split(".")[0] + out_filename + str(experiment)
#verbose
if verbose: print "Using: ", motif_filename, " as input"
if verbose: print "Writing to (suffix): ", "[void]" if out_filename==""\
else out_filename
#open files for ouput
try:
out_file = open(out_filename + ".csv","w")
except (IOError, OSError) as file_open_exception:
print "*** Something went wrong while opening the output file"
print "*** Error: ", file_open_exception.errno, " - ",\
file_open_exception.strerror
sys.exit()
#open file for error recording
try:
err_file = open(out_filename+".err","w")
except (IOError, OSError) as file_open_exception:
print "Something went wrong while opening the error file"
sys.exit()
#compute priors
PR=rproms/tproms #prior probability of regulation
PB=1.0-PR #prior probability of non-regulation
PPR=PB/PR #prior probability ratio
#read motif and assing 0.25 pseudocounts to PSWM
#also assign background uniform distribution for the PSSM (default)
mot = MGlib.read_motif(motif_filename)
mot.pseudocounts=0.25
mot.background=None
#save the pssm for the motif and the reverse complement
#(so that they are not recalculated everytime we invoke motif.pssm)
pssm = mot.pssm
rpssm = pssm.reverse_complement()
if (experiment==0):
#-------------------------------------------------------------------------
#Experiment 0:
#10000 sequences, with 100% on average having random pseudo-sites inserted
#all sites are inserted at the first position of the sequence
#get individual sequence posteriors and write them together with the
#score of the site inserted
random.seed(None)
#create background sequence set: 100 seqs 283 bp long
set1 = MGlib.random_DNA(283,{'A': 0.3,'C': 0.2,'G': 0.2,'T': 0.3},10000)
#compute softmax scores for background sequences in dataset
gscr = MGlib.sfmax_score_seqs(set1,pssm,rpssm)
#get normal distributions for background and motif
n_g=norm(mean(gscr), std(gscr))
n_m=norm(pssm.mean(), pssm.std())
#create motif instances
pmot1 = MGlib.sample_motif(mot,1000)
#insert sites in sequences
e=0
while (e<len(set1)):
r = random.random()
#determine if site is to be inserted and insert random site
if (r<1):
set1[e] = random.choice(pmot1) + set1[e]
#otherwise insert random sequence from own sequence start
else :
set1[e] = set1[e][:17] + set1[e]
e = e+1
#compute softmax scores for sequences in dataset
scrs=MGlib.sfmax_score_seqs(set1,pssm,rpssm)
#get log-likelihoods for sequences in dataset
llrs=MGlib.ll_ratios(scrs,n_g,n_m,alpha)
#get per-sequence posterior for the sequences in dataset
fposts=MGlib.PostP(llrs,PPR,1)
#write results to file
e=0
while (e<len(set1)):
out_file.write(str(scrs[e][0]))
out_file.write(',')
out_file.write(str(fposts[e]))
out_file.write('\n')
e=e+1
out_file.close()
return 0
elif (experiment==1):
#-------------------------------------------------------------------------
#Experiment 1:
#2x100 sequences, with the first 10 having random pseudo-sites inserted
#all sites are inserted at the first position of the sequence
#get individual sequence posteriors and write them together with the
#score of the site inserted
#first sequence set includes randomly distributed sites
#second sequence set includes only one site
#experiment is repeated 100 times
random.seed(None)
for cnt in range(0,100):
#create background sequence set: 100 seqs 283 bp long
set1 = MGlib.random_DNA(283,{'A': 0.3,'C': 0.2,'G': 0.2,'T': 0.3},100)
set2 = set1[:]
#compute softmax scores for background sequences in dataset
gscr = MGlib.sfmax_score_seqs(set1,pssm,rpssm)
#get normal distributions for background and motif
n_g=norm(mean(gscr), std(gscr))
n_m=norm(pssm.mean(), pssm.std())
#create motif instances
pmot1 = MGlib.sample_motif(mot,100)
pmot2 = MGlib.sample_motif(mot,1)
print cnt
#insert sites in sequences
e=0
while (e<len(set1)):
r = random.random()
#insert random site in first 10 sequences
if (e<11):
set1[e] = random.choice(pmot1) + set1[e]
set2[e] = random.choice(pmot2) + set2[e]
#otherwise insert random sequence from own sequence start
else :
set1[e] = set1[e][:17] + set1[e]
set2[e] = set2[e][:17] + set2[e]
e = e+1
#compute softmax scores for sequences in dataset
scrs1=MGlib.sfmax_score_seqs(set1,pssm,rpssm)
scrs2=MGlib.sfmax_score_seqs(set2,pssm,rpssm)
if verbose: print "varied"
if verbose:
for s in scrs1: print s[0]
if verbose: print "loners"
if verbose:
for s in scrs2: print s[0]
#get log-likelihoods for sequences in dataset
llrs1=MGlib.ll_ratios(scrs1,n_g,n_m,alpha)
llrs2=MGlib.ll_ratios(scrs2,n_g,n_m,alpha)
#get overall posterior for the sequences in dataset
fposts1=MGlib.PostP(llrs1,PPR,0)
fposts2=MGlib.PostP(llrs2,PPR,0)
#write results to file
out_file.write(str(fposts1))
out_file.write(',')
out_file.write(str(fposts2))
out_file.write('\n')
out_file.close()
return 0