def print_meme_motif(word, nsites, ev_string, aln, alph):
# make the PWM
pwm = sequence.PWM(alph)
pwm.setFromAlignment(aln)
# print PWM in MEME format
alen = alph.getLen()
w = len(word)
print "\nMOTIF %s\nletter-probability matrix: alength= %d w= %d nsites= %d E= %s" % \
(word, alen, w, nsites, ev_string)
for row in pwm.pretty():
print row
print ""
def print_meme_motif(word, nsites, ev_string, aln):
# make the PWM
pwm = sequence.PWM(sequence.getAlphabet('DNA'))
pwm.setFromAlignment(aln)
# print PWM in MEME format
alphabet = sequence.getAlphabet('DNA').getSymbols()
alen = len(alphabet)
w = len(word)
print "\nMOTIF %s\nletter-probability matrix: alength= %d w= %d nsites= %d E= %s" % \
(word, alen, w, nsites, ev_string)
for row in pwm.pretty():
print row
print ""
def get_freqs_and_nsites(w, dists, offsets, seqs, alph):
"""
Get PWMs and numbers of sites for each exact Hamming distance.
Returns (freqs[d], nsites[d]).
"""
freqs = []
nsites = []
# iterate over hamming distances
for d in range(w+1):
# get alignments for a hamming distance == d
aln = get_aln_from_dists_and_offsets(w, d, d, dists, offsets, seqs, alph)
# make frequency matrix
pwm = sequence.PWM(alph)
pwm.setFromAlignment(aln)
freqs.append(pwm.getFreq())
nsites.append(len(aln))
#print "getting freqs for d", d
return freqs, nsites
def get_freqs_and_nsites(w, dists, offsets, seqs):
"""
Get PWMs and numbers of sites for each exact Hamming distance.
Returns (freqs[d], nsites[d]).
"""
freqs = []
nsites = []
for d in range(w + 1): # Hamming distance
# get alignments
aln = get_aln_from_dists_and_offsets(w, d, d, dists, offsets, seqs)
# make frequency matrix
#FIXME alphabet
pwm = sequence.PWM(sequence.getAlphabet('DNA'))
pwm.setFromAlignment(aln)
freqs.append(pwm.getFreq())
nsites.append(len(aln))
#print "getting freqs for d", d
return freqs, nsites
def scanMotifReport(seqs, motif, threshold=0, jaspar = 'JASPAR_matrices.txt'):
""" Produce a plot for a scan of the specified motif.
The plot has as its x-axis position of sequence, and
the y-axis the cumulative, non-negative PWM score over all sequences. """
# check that all sequences are the same length and set sequence length
seq_len = len(seqs[0])
for seq in seqs:
if len(seq) != seq_len:
usage(sys.argv[0], "All sequences must have same length")
return
# create the motif and its reverse complemennt
bg = prb.Distrib(sym.DNA_Alphabet, sequence.getCount(seqs))
d = prb.readMultiCounts(jaspar)
try:
fg1 = d[motif]
fg2 = getReverse(d[motif])
except KeyError:
usage(sys.argv[0], "Unknown motif %s" % motif)
return
print("Motif %s:" % motif)
pwm1 = sequence.PWM(fg1, bg)
pwm1.display(format='JASPAR')
print("Motif %s (reverse complement):" % motif)
pwm2 = sequence.PWM(fg2, bg)
pwm2.display(format='JASPAR')
# initialize things to zero
avg_motif_score = np.zeros(seq_len)
# compute average score at each position (on both strands) in sequences
i_seq = 0
motif_width = pwm1.length
for seq in seqs:
i_seq += 1
# print >> sys.stderr, "Scoring seq: %4d\r" % (i_seq),
# positive strand
hits = pwm1.search(seq, threshold)
pos_scores = seq_len * [0]
for hit in hits:
# mark hit at *center* of site (hence motif_width/2)
pos_scores[hit[0]+(motif_width/2)] = hit[2]
# negative strand
hits = pwm2.search(seq, threshold)
neg_scores = seq_len * [0]
for hit in hits:
neg_scores[hit[0]+(motif_width/2)] = hit[2]
# use maximum score on two strands
for i in range(seq_len):
score = max(pos_scores[i], neg_scores[i])
if (score > threshold):
avg_motif_score[i] += score
# compute average score
for i in range(seq_len):
avg_motif_score[i] /= len(seqs)
# hw = 5 # window width is 2*hw + 1
# smoothed_avg_motif_score = np.zeros(seq_len)
# for i in range(hw, seq_len-motif_width+1-hw):
# smoothed_avg_motif_score[i]=sum(avg_motif_score[i-hw:i+hw+1])/(2*hw+1)
# plot the average score curve
# print >> sys.stderr, ""
x = list(range(-(seq_len/2), (seq_len/2))) # call center of sequence X=0
lbl = "%s" % (motif)
plt.plot(x, avg_motif_score, label=lbl)
#plt.plot(x, smoothed_avg_motif_score, label=lbl)
plt.axhline(color='black', linestyle='dotted')
plt.legend(loc='lower center')
plt.xlabel('position')
plt.ylabel('average motif score')
plt.title(motif)
plt.show()
def scanMotifReport_new(seqs, motif, threshold=3.4567, jaspar = 'JASPAR_matrices.txt', seed=0):
""" Produce a plot for a scan of the specified motif.
The plot has as its x-axis position of sequence, and
the y-axis the number of sequences with a best hit at position x.
Sequences with no hit above 'threshold' are ignored.
Ties for best hit are broken randomly.
The p-value of the central region that is most "centrally enriched"
and the width of the best central region is printed in the label
of the plot.
"""
# set the random seed for repeatability
random.seed(seed)
# Copy the code from your "improved" version of scanMotifReport()
# to here, and follow the instructions in the Prac to develop this
# new function.
# check that all sequences are the same length and set sequence length
seq_len = len(seqs[0])
for seq in seqs:
if len(seq) != seq_len:
usage(sys.argv[0], "All sequences must have same length")
return
# create the motif and its reverse complemennt
bg = prb.Distrib(sym.DNA_Alphabet, sequence.getCount(seqs))
d = prb.readMultiCounts(jaspar)
try:
fg1 = d[motif]
fg2 = getReverse(d[motif])
except KeyError:
usage(sys.argv[0], "Unknown motif %s" % motif)
return
print("Motif %s:" % motif)
pwm1 = sequence.PWM(fg1, bg)
pwm1.display(format='JASPAR')
print("Motif %s (reverse complement):" % motif)
pwm2 = sequence.PWM(fg2, bg)
pwm2.display(format='JASPAR')
# initialize things to zero
hit_count = np.zeros(seq_len)
n_seqs_with_hits = 0.0
# Scan each sequence for all hits on both strands and record
# the number of "best hits" at each sequence position.
#
motif_width = pwm1.length
i_seq = 0
for seq in seqs:
i_seq += 1
# print >> sys.stderr, "Scoring seq: %4d\r" % (i_seq),
# scan with both motifs
hits = pwm1.search(seq, threshold) + pwm2.search(seq, threshold)
# Record position of best hit
if (hits):
n_seqs_with_hits += 1
# find best hit score
best_score = max(hits, key=operator.itemgetter(1))[2]
# find ties
best_hits = [ hit for hit in hits if hit[2] == best_score ]
# break ties at random
best_hit = random.choice(best_hits)
# mark hit at *center* of site (hence pwm1.length/2)
hit_count[best_hit[0] + pwm1.length/2] += 1
# divide number of sequences with hit by total number of hits
site_probability = [ (cnt/n_seqs_with_hits) for cnt in hit_count ]
print("Number of sequences with hit (score >= %f): %d" % (threshold, n_seqs_with_hits), file=sys.stderr)
# STATISTICS
# Get the cumulative hit counts in concentric windows
# and perform the Binomial Test. Report best region and its p-value.
#
best_r = 0
best_log_pvalue = 1
center = seq_len/2 # center of sequence
cum_hit_count = np.zeros(seq_len) # total hits in window of width i
for i in range(1, (seq_len - pwm1.length/2 + 1)/2):
cum_hit_count[i] = cum_hit_count[i-1] + hit_count[center-i] + hit_count[center+i]
# Compute probability of observed or more best hits in central window
# assuming uniform probability distribution in each sequence.
# successes = cum_hit_count[i]
# trials = n_seqs_with_hits
# p_success = ?
# log_pvalue = ?
# if (log_pvalue < best_log_pvalue):
# best_log_pvalue = log_pvalue
# best_r = 2*i
# End STATISTICS
hw = 5
smoothed_site_probability = np.zeros(seq_len)
for i in range(hw, seq_len-motif_width+1-hw):
smoothed_site_probability[i]=sum(site_probability[i-hw:i+hw+1])/(2*hw+1)
x = list(range(-(seq_len/2), (seq_len/2))) # call center of sequence X=0
lbl = "%s, t=%.2f" % (motif, threshold)
#lbl = "%s, t=%.2f, w=%d, p=%.2e" % (motif, threshold, best_r, math.exp(best_log_pvalue))
plt.plot(x, smoothed_site_probability, label=lbl)
plt.axhline(color='black', linestyle='dotted')
plt.legend(loc='lower center')
plt.xlabel('Position of best site')
plt.ylabel('Smoothed probability')
plt.title(motif)
plt.show()
