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()