def main(list_sequence_names, output_prefix):
list_sequences = [] #list of list of sequences, where each item represents a label
extended_list_sequences = [] #flat list of sequences
labels = [] #labels for list_sequences
for [filename, label] in list_sequence_names:
sequences = seq_IO.read_sequences(filename)
list_sequences.append(sequences)
extended_list_sequences.extend(sequences[:])
labels.append(label)
cleaved_ind = labels.index("CLEAVED")
middle_ind = labels.index("MIDDLE")
uncleaved_ind = labels.index("UNCLEAVED")
frac_uncleaved = {}
frac_cleaved = {}
frac_middle = {}
for seq in list_sequences[cleaved_ind]:
cleaved_seqs = sum([1 for s in list_sequences[cleaved_ind] if conv.hamdist(seq,s) == 1])
uncleaved_seqs = sum([1 for s in list_sequences[uncleaved_ind] if conv.hamdist(seq,s) == 1])
middle_seqs = sum([1 for s in list_sequences[middle_ind] if conv.hamdist(seq,s) == 1])
if cleaved_seqs > 0 or uncleaved_seqs > 0:
total = uncleaved_seqs+middle_seqs+cleaved_seqs
frac_uncleaved[seq] = float(uncleaved_seqs)/total
frac_cleaved[seq] = float(cleaved_seqs)/total
frac_middle[seq] = float(middle_seqs)/total
fig, ax = pconv.create_ax(3, 1)
hist.draw_actual_plot(ax[0,0], frac_cleaved.values(), "Landscape Near Cleaved Sequences", "Fraction of Neighbors Cleaved", log=False, normed=False, nbins=20)
hist.draw_actual_plot(ax[0,1], frac_middle.values(), "Landscape Near Cleaved Sequences", "Fraction of Neighbors Middle", log=False, normed=False, nbins=20)
hist.draw_actual_plot(ax[0,2], frac_uncleaved.values(), "Landscape Near Cleaved Sequences", "Fraction of Neighbors Uncleaved", log=False, normed=False, nbins=20)
pconv.save_fig(fig, output_prefix, "fraction_neighbors", 15, 5, size=10)
def main(sequence_list, canonical_seq_list, known_cleaved):
sequences = seq_IO.read_sequences(sequence_list, additional_params=True)
canonical_seqs = seq_IO.read_sequences(canonical_seq_list)
known_cleaved_list = seq_IO.read_sequences(known_cleaved)
base = os.path.splitext(sequence_list)[0]
cleaved_seqs = [ (s[0],s[1],s[2],min([conv.hamdist(s[0],c) for c in canonical_seqs])) for s in sequences if s[1] == 'CLEAVED' and s[2] > 2.0 and s[0] not in known_cleaved_list]
uncleaved_seqs = [ (s[0],s[1],s[2],min([conv.hamdist(s[0],c) for c in canonical_seqs])) for s in sequences if s[1] == 'UNCLEAVED' and s[2] < -2.0 and s[0] not in known_cleaved_list]
cl_s_dist = [ s[2] for s in cleaved_seqs]
uncl_s_dist = [s[2] for s in uncleaved_seqs]
print max(cl_s_dist)
print min(uncl_s_dist)
cleaved_seqs_low_ham = sorted(cleaved_seqs, key=lambda x: (x[3], -x[2]))[0:4]
cleaved_seqs_hi_ham = sorted(cleaved_seqs, key=lambda x: (-x[3], -x[2]))[0:4]
uncleaved_seqs_low_ham = sorted(uncleaved_seqs, key=lambda x: (x[3], x[2]))[0:4]
uncleaved_seqs_hi_ham = sorted(uncleaved_seqs, key=lambda x: (-x[3], x[2]))[0:4]
outfile = '%s_selected.csv' % (base)
out = open(outfile,"w")
out.write("Cleaved_seqs_low_hamming_distance\n")
out.write("\n".join( [ ",".join(map(str,s)) for s in cleaved_seqs_low_ham ] ))
out.write("\nCleaved_seqs_high_hamming_distance\n")
out.write("\n".join( [ ",".join(map(str,s)) for s in cleaved_seqs_hi_ham ] ))
out.write("\nUncleaved_seqs_low_hamming_distance\n")
out.write("\n".join( [ ",".join(map(str,s)) for s in uncleaved_seqs_low_ham ] ))
out.write("\nUncleaved_seqs_high_hamming_distance\n")
out.write("\n".join( [ ",".join(map(str,s)) for s in uncleaved_seqs_hi_ham ] ))
def main(sequence_list, trained_cleaved, trained_uncleaved):
sequences = seq_IO.read_sequences(sequence_list, additional_params=True)
trained_cleaved_list = seq_IO.read_sequences(trained_cleaved)
trained_uncleaved_list = seq_IO.read_sequences(trained_uncleaved)
base = os.path.splitext(sequence_list)[0]
cleaved_seqs = [(s[0], s[1],
min([conv.hamdist(s[0], c)
for c in trained_cleaved_list])) for s in sequences
if s[1] == 'CLEAVED']
uncleaved_seqs = [
(s[0], s[1],
min([conv.hamdist(s[0], c) for c in trained_uncleaved_list]))
for s in sequences if s[1] == 'UNCLEAVED'
]
outfile = '%s_selected_hamm.csv' % (base)
out = open(outfile, "w")
out.write("Cleaved_seqs\n")
out.write("\n".join([",".join(map(str, s)) for s in cleaved_seqs]))
out.write("\nUncleaved_seqs\n")
out.write("\n".join([",".join(map(str, s)) for s in uncleaved_seqs]))
def main(list_sequence_names, hamming_dist, output_prefix, canonical_file):
list_sequences = [] #list of list of sequences, where each item represents a label
extended_list_sequences = [] #flat list of sequences
labels = [] #labels for list_sequences
#canonical_seqs = seq_IO.read_sequences(canonical_file)
canonical_seqs = ["DEMEE"] #left other code here in case want to try it from all cleaved sequences
dict_sequences = {}
for [filename, label] in list_sequence_names:
sequences = seq_IO.read_sequences(filename, additional_params=True, ind_type={1:float})
new_seqs = [ (seq,fitness,min([ conv.hamdist(seq,can) for can in canonical_seqs ]) <= 2) for seq,fitness in sequences ]
list_sequences.append(new_seqs)
extended_list_sequences.extend(new_seqs[:])
dict_sequences.update({ n[0] : n for n in new_seqs })
labels.append(label)
edges = []
edges_set = set()
print "Read in Data: {0}".format(datetime.datetime.now())
for seq, fitness, canonical_like in extended_list_sequences:
neighbors = conv.gen_hamdist_one(seq)
edges_set.update([ (seq, n) for n in neighbors if n in dict_sequences ])
edges += [((seq, fitness, canonical_like), dict_sequences[n] ) for n in neighbors if n in dict_sequences and (n,seq) not in edges_set ]
print "Generated Edges: {0}".format(datetime.datetime.now())
print edges[0:10]
seq_id = { seq[0] : ind for ind, seq in enumerate(extended_list_sequences) }
nodes = []
for seqs, label in zip(list_sequences, labels):
nodes.extend([ { "id" : seq_id[seq[0]], "sequence" : seq[0], "status" : label, "fitness" : seq[1], "canonical_like" : seq[2] } for seq in seqs ])
print "Generated List of Nodes: {0}".format(datetime.datetime.now())
links = []
for canonical_seq in canonical_seqs:
print canonical_seq
for ((seq1,fit1,can1),(seq2,fit2,can2)) in edges:
dist_seq1 = conv.hamdist(canonical_seq, seq1)
dist_seq2 = conv.hamdist(canonical_seq, seq2)
fit_lower = fit1 if dist_seq1 < dist_seq2 else fit2
fit_upper = fit2 if dist_seq1 < dist_seq2 else fit1
fit_upper = fit_upper if fit_upper > 0 else 0.001
seq_lower = seq1 if dist_seq1 < dist_seq2 else seq2
seq_upper = seq2 if dist_seq1 < dist_seq2 else seq1
links.append({ "source" : seq_id[seq_lower], "target" : seq_id[seq_upper], "weight" : fit_lower/float(fit_upper) } )
print "Generated List of Edges: {0}".format(datetime.datetime.now())
output = { "nodes" : nodes, "links" : links }
with open('{0}nodes_edges.json'.format(output_prefix), 'w') as fp:
json.dump(output, fp)
print "Dumped Nodes and Edges Lists: {0}".format(datetime.datetime.now())
def main(list_sequence_names, hamming_dist, output_prefix, canonical_file):
list_sequences = [] #list of list of sequences, where each item represents a label
extended_list_sequences = [] #flat list of sequences
labels = [] #labels for list_sequences
#canonical_seqs = seq_IO.read_sequences(canonical_file)
canonical_seqs = ["DEMEE"] #left other code here in case want to try it from all cleaved sequences
for [filename, label] in list_sequence_names:
sequences = seq_IO.read_sequences(filename, additional_params=True, ind_type={1:float})
new_seqs = [ (seq,fitness,min([ conv.hamdist(seq,can) for can in canonical_seqs ]) <= 2) for seq,fitness in sequences ]
list_sequences.append(new_seqs)
extended_list_sequences.extend(new_seqs[:])
dict_sequences = { n[0] : n for n in new_seqs }
labels.append(label)
edges = []
edges_set = set()
print "Read in Data: {0}".format(datetime.datetime.now())
for seq, fitness, canonical_like in extended_list_sequences:
neighbors = conv.gen_hamdist_one(seq)
edges_set.update([ (seq, n) for n in neighbors if n in dict_sequences ])
edges += [((seq, fitness, canonical_like), dict_sequences[n] ) for n in neighbors if n in dict_sequences and (n,seq) not in edges_set ]
print "Generated Edges: {0}".format(datetime.datetime.now())
print edges[0:10]
seq_id = { seq[0] : ind for ind, seq in enumerate(extended_list_sequences) }
nodes = []
for seqs, label in zip(list_sequences, labels):
nodes.extend([ { "id" : seq_id[seq[0]], "sequence" : seq[0], "status" : label, "fitness" : seq[1], "canonical_like" : seq[2] } for seq in seqs ])
print "Generated List of Nodes: {0}".format(datetime.datetime.now())
links = []
for canonical_seq in canonical_seqs:
print canonical_seq
for ((seq1,fit1,can1),(seq2,fit2,can2)) in edges:
dist_seq1 = conv.hamdist(canonical_seq, seq1)
dist_seq2 = conv.hamdist(canonical_seq, seq2)
fit_lower = fit1 if dist_seq1 < dist_seq2 else fit2
fit_upper = fit2 if dist_seq1 < dist_seq2 else fit1
fit_upper = fit_upper if fit_upper > 0 else 0.001
seq_lower = seq1 if dist_seq1 < dist_seq2 else seq2
seq_upper = seq2 if dist_seq1 < dist_seq2 else seq1
links.append({ "source" : seq_id[seq_lower], "target" : seq_id[seq_upper], "weight" : fit_lower/float(fit_upper) } )
print "Generated List of Edges: {0}".format(datetime.datetime.now())
output = { "nodes" : nodes, "links" : links }
with open('{0}nodes_edges.json'.format(output_prefix), 'w') as fp:
json.dump(output, fp)
print "Dumped Nodes and Edges Lists: {0}".format(datetime.datetime.now())
def main(list_sequence_names, hamming_dist, output_prefix, canonical_file):
list_sequences = [
] #list of list of sequences, where each item represents a label
extended_list_sequences = [] #flat list of sequences
labels = [] #labels for list_sequences
canonical_seqs = seq_IO.read_sequences(canonical_file)
for [filename, label] in list_sequence_names:
sequences = seq_IO.read_sequences(filename,
additional_params=True,
ind_type={1: float})
new_seqs = [(seq, fitness,
min([conv.hamdist(seq, can)
for can in canonical_seqs]) <= 2)
for seq, fitness in sequences]
list_sequences.append(new_seqs)
extended_list_sequences.extend(new_seqs[:])
labels.append(label)
outfile_nodes = '%s_nodes.csv' % (output_prefix)
edges = [
(seq2, seq)
for seq, seq2 in itertools.combinations(extended_list_sequences, 2)
if conv.hamdist(seq2[0], seq[0]) == hamming_dist
]
for canonical_seq in canonical_seqs:
outfile_edges = '%s_%s_edges.csv' % (output_prefix, canonical_seq)
edges_out = open(outfile_edges, "w")
edges_out.write("Source,Target,Weight\n")
print canonical_seq
for ([seq1, fit1, can1], [seq2, fit2, can2]) in edges:
dist_seq1 = conv.hamdist(canonical_seq, seq1)
dist_seq2 = conv.hamdist(canonical_seq, seq2)
fit_lower = fit1 if dist_seq1 < dist_seq2 else fit2
fit_upper = fit2 if dist_seq1 < dist_seq2 else fit1
fit_upper = fit_upper if fit_upper > 0 else 0.001
seq_lower = seq1 if dist_seq1 < dist_seq2 else seq2
seq_upper = seq2 if dist_seq1 < dist_seq2 else seq1
out_str = "{0},{1},{2}\n".format(seq_lower, seq_upper,
fit_lower / float(fit_upper))
edges_out.write(
out_str) #does this have the correct directionality?
edges_out.close()
already_written_nodes = []
nodes_out = open(outfile_nodes, "w")
nodes_out.write("Id,Label,Type,Fitness,Canonical\n")
for seqs, label in zip(list_sequences, labels):
nodes_out.write("\n".join(
"{0},{0},{1},{2},{3}".format(x, label, fitness, can)
for (x, fitness, can) in seqs if x not in already_written_nodes))
already_written_nodes.extend([s[0] for s in seqs])
nodes_out.write("\n")
def main(sequence_list, canonical_seq_list, known_cleaved):
sequences = seq_IO.read_sequences(sequence_list, additional_params=True)
canonical_seqs = seq_IO.read_sequences(canonical_seq_list)
known_cleaved_list = seq_IO.read_sequences(known_cleaved)
base = os.path.splitext(sequence_list)[0]
cleaved_seqs = [
(s[0], s[1], s[2], min([conv.hamdist(s[0], c)
for c in canonical_seqs])) for s in sequences
if s[1] == 'CLEAVED' and s[2] > 2.0 and s[0] not in known_cleaved_list
]
uncleaved_seqs = [(s[0], s[1], s[2],
min([conv.hamdist(s[0], c) for c in canonical_seqs]))
for s in sequences if s[1] == 'UNCLEAVED' and s[2] < -2.0
and s[0] not in known_cleaved_list]
cl_s_dist = [s[2] for s in cleaved_seqs]
uncl_s_dist = [s[2] for s in uncleaved_seqs]
print max(cl_s_dist)
print min(uncl_s_dist)
cleaved_seqs_low_ham = sorted(cleaved_seqs, key=lambda x:
(x[3], -x[2]))[0:4]
cleaved_seqs_hi_ham = sorted(cleaved_seqs, key=lambda x:
(-x[3], -x[2]))[0:4]
uncleaved_seqs_low_ham = sorted(uncleaved_seqs, key=lambda x:
(x[3], x[2]))[0:4]
uncleaved_seqs_hi_ham = sorted(uncleaved_seqs, key=lambda x:
(-x[3], x[2]))[0:4]
outfile = '%s_selected.csv' % (base)
out = open(outfile, "w")
out.write("Cleaved_seqs_low_hamming_distance\n")
out.write("\n".join([",".join(map(str, s)) for s in cleaved_seqs_low_ham]))
out.write("\nCleaved_seqs_high_hamming_distance\n")
out.write("\n".join([",".join(map(str, s)) for s in cleaved_seqs_hi_ham]))
out.write("\nUncleaved_seqs_low_hamming_distance\n")
out.write("\n".join(
[",".join(map(str, s)) for s in uncleaved_seqs_low_ham]))
out.write("\nUncleaved_seqs_high_hamming_distance\n")
out.write("\n".join([",".join(map(str, s))
for s in uncleaved_seqs_hi_ham]))
def find_seqs_more_than_first(can, sequences, set_sequences, hamm_dist):
if hamm_dist == -1:
set_sequences = set([seq for seq in sequences if chem_sim(seq, can)])
else:
set_sequences = set(
[seq for seq in sequences if conv.hamdist(seq, can) > hamm_dist])
return set_sequences
def main(input_dir, canonical_file, output_prefix, hamm_dist):
list_seq_files = glob.glob(os.path.join(input_dir, "*_cleaved.txt"))
dict_sequences = {}
canonical_sequences = []
canonical_sequences = seq_IO.read_sequences(canonical_file)
for filename in list_seq_files:
sequences = seq_IO.read_sequences(filename)
for can in canonical_sequences:
if hamm_dist == -1:
seq_sim = [seq for seq in sequences if chem_sim(seq, can)]
else:
seq_sim = [
seq for seq in sequences
if conv.hamdist(seq, can) <= hamm_dist
]
if seq_sim:
dict_sequences[(filename, can)] = seq_sim
outfile_canon = '%scanonical_sim_cleaved%d.csv' % (output_prefix,
hamm_dist)
canon_out = open(outfile_canon, "w")
for (filename, can), seqs in dict_sequences.items():
canon_out.write(filename + "," + can + "," + ','.join(seqs) + "\n")
def find_seqs_less_than(can, sequences, set_sequences, hamm_dist):
if hamm_dist == -1:
set_sequences = set_sequences.union(
[seq for seq in sequences if chem_sim(seq, can)])
else:
set_sequences = set_sequences.union(
[seq for seq in sequences if conv.hamdist(seq, can) <= hamm_dist])
return set_sequences
def main(list_sequence_names, output_prefix, canonical_file):
list_sequences = [] #list of list of sequences, where each item represents a label
extended_list_sequences = [] #flat list of sequences
labels = [] #labels for list_sequences
canonical_seqs = seq_IO.read_sequences(canonical_file)
for [filename, label] in list_sequence_names:
sequences = seq_IO.read_sequences(filename, additional_params=True, ind_type={1:float})
list_sequences.append(sequences)
extended_list_sequences.extend(sequences[:])
labels.append(label)
dict_sequences = { seq : fitness for (seq, fitness) in extended_list_sequences }
epi = {}
for canonical_seq in canonical_seqs:
mut_func = { "Both_Functional" : [], "Both_Nonfunctional" : [], "One_Functional" : [] }
mut_nonfunc = { "Both_Functional" : [], "Both_Nonfunctional" : [], "One_Functional" : [] }
outfile_epi = '%s_%s_epi.csv' % (output_prefix, canonical_seq)
epi_out = open(outfile_epi,"w")
print canonical_seq
epi = {}
double_mut = [ seq for seq in extended_list_sequences if conv.hamdist(canonical_seq, seq[0]) == 2 ]
for seq_fit in extended_list_sequences:
seq = seq_fit[0]
fit = seq_fit[1]
mut_dict = mut_func if fit == 1000 else mut_nonfunc
list_fit = get_inter_fitness(canonical_seq, seq, dict_sequences)
if len(list_fit) <= 1:
continue
if all(list_fit):
if seq_fit in double_mut:
sum_fit = sum(list_fit)
print sum_fit
if sum_fit == 2000:
mut_dict["Both_Functional"].append((canonical_seq, seq))
elif sum_fit == 0:
mut_dict["Both_Nonfunctional"].append((canonical_seq, seq))
elif sum_fit == 1000:
mut_dict["One_Functional"].append((canonical_seq, seq))
epi[seq] = (calc_epi(list_fit, fit),list_fit+[fit])
epi_out.write("Total Double Mutants,%s\n" % (len(double_mut)))
for label, list_muts in mut_func.items():
for (can, seq) in list_muts:
epi_out.write("End Functional,%s,%s,%s\n" % (label,can,seq) )
for label, list_muts in mut_nonfunc.items():
for (can, seq) in list_muts:
epi_out.write("End Functional,%s,%s,%s\n" % (label,can,seq) )
epi_out.write("\n".join(["{0},{1},{2}".format(seq,epi,",".join([str(f) for f in fits])) for seq, (epi,fits) in epi.items()] ) )
epi_out.close()
def main(list_sequence_names, hamming_dist, output_prefix, canonical_file):
list_sequences = [] #list of list of sequences, where each item represents a label
extended_list_sequences = [] #flat list of sequences
labels = [] #labels for list_sequences
canonical_seqs = seq_IO.read_sequences(canonical_file)
for [filename, label] in list_sequence_names:
sequences = seq_IO.read_sequences(filename, additional_params=True, ind_type={1:float})
new_seqs = [ (seq,fitness,min([ conv.hamdist(seq,can) for can in canonical_seqs ]) <= 2) for seq,fitness in sequences ]
list_sequences.append(new_seqs)
extended_list_sequences.extend(new_seqs[:])
labels.append(label)
outfile_nodes = '%s_nodes.csv' % (output_prefix)
edges = [(seq2,seq) for seq,seq2 in itertools.combinations(extended_list_sequences,2) if conv.hamdist(seq2[0],seq[0]) == hamming_dist ]
for canonical_seq in canonical_seqs:
outfile_edges = '%s_%s_edges.csv' % (output_prefix, canonical_seq)
edges_out = open(outfile_edges,"w")
edges_out.write("Source,Target,Weight\n")
print canonical_seq
for ([seq1,fit1,can1],[seq2,fit2,can2]) in edges:
dist_seq1 = conv.hamdist(canonical_seq, seq1)
dist_seq2 = conv.hamdist(canonical_seq, seq2)
fit_lower = fit1 if dist_seq1 < dist_seq2 else fit2
fit_upper = fit2 if dist_seq1 < dist_seq2 else fit1
fit_upper = fit_upper if fit_upper > 0 else 0.001
seq_lower = seq1 if dist_seq1 < dist_seq2 else seq2
seq_upper = seq2 if dist_seq1 < dist_seq2 else seq1
out_str = "{0},{1},{2}\n".format(seq_lower,seq_upper,fit_lower/float(fit_upper))
edges_out.write(out_str) #does this have the correct directionality?
edges_out.close()
already_written_nodes = []
nodes_out = open(outfile_nodes,"w")
nodes_out.write("Id,Label,Type,Fitness,Canonical\n")
for seqs,label in zip(list_sequences,labels):
nodes_out.write("\n".join("{0},{0},{1},{2},{3}".format(x, label, fitness,can) for (x,fitness,can) in seqs if x not in already_written_nodes))
already_written_nodes.extend([ s[0] for s in seqs])
nodes_out.write("\n")
def main(list_sequence_names, canonical_list, output_prefix, func_labels,
unfunc_labels):
series = []
canonical_list_seq = seq_IO.read_sequences(canonical_list)
for canonical in canonical_list_seq:
dict_sequences = {}
for [filename, label] in list_sequence_names:
sequences = seq_IO.read_sequences(filename)
distances = [conv.hamdist(seq, canonical) for seq in sequences]
dict_sequences[label] = {
i: sum([d for d in distances if d == i])
for i in xrange(1, 6)
}
x = []
y = []
for i in xrange(1, 6):
func = 0.0
unfunc = 0.0
for label, dict_sums in dict_sequences.items():
if label in func_labels:
func = func + dict_sums[i]
elif label in unfunc_labels:
unfunc = unfunc + dict_sums[i]
if unfunc != 0:
x.append(i)
y.append(func / (func + unfunc))
print x
print y
series.append([x, y, canonical])
fig, ax = pconv.create_ax(1, 1)
scatterplot.plot_series(ax[0, 0],
series,
title="",
x_axis="# of Mutations",
y_axis="Fraction of Variants that are Functional",
alpha=1.0,
connect_dots=True,
size=30,
edgecolors='k')
ax[0, 0].set_xlim(xmin=1, xmax=5)
ax[0, 0].set_xticks(xrange(1, 6))
pconv.save_fig(fig,
output_prefix,
canonical + "_fraction_func_mutant",
6,
6,
size=15)
def main(json_file, output_prefix, novel_seqs_file, canonical_file):
print "Started Script: {0}".format(datetime.datetime.now())
with open(json_file) as data_file:
data = json.load(data_file)
G = json_graph.node_link_graph(data, directed=False)
print "Finished Reading in Graph: {0}".format(datetime.datetime.now())
id_seq = networkx.get_node_attributes(G, "sequence")
id_status = networkx.get_node_attributes(G, "status")
seq_id = { seq : node_id for node_id, seq in id_seq.items()}
print "Created inverse lookup table: {0}".format(datetime.datetime.now())
novel_seqs = seq_IO.read_sequences(novel_seqs_file)
canonical_seqs = seq_IO.read_sequences(canonical_file)
novel_fracs = {}
print "Ready to enter loop: {0}".format(datetime.datetime.now())
for n in novel_seqs:
novel_fracs[n] = {}
hamm_dist = sorted([ (conv.hamdist(n,c),c) for c in canonical_seqs ])
min_hamm_dist = hamm_dist[0][0]
print "Found hamming distances: {0}".format(datetime.datetime.now())
for hamm, c in hamm_dist:
#only analyze min_dist canonical sequences
if hamm != min_hamm_dist:
continue
novel_fracs[n][c] = []
#generate list of 5 paths
#paths = itertools.islice(networkx.all_shortest_paths(G, seq_id[n], seq_id[c]), 5)
paths = [ networkx.shortest_path(G, seq_id[n], seq_id[c]) ]
for path in paths:
inter_nodes = path[1:-1]
novel_fracs[n][c].append(float(sum([ 1 for node_id in inter_nodes if id_status[node_id] == "UNCLEAVED" ]))/len(inter_nodes))
base_n_file = os.path.basename(os.path.splitext(novel_seqs_file)[0])
base_c_file = os.path.basename(os.path.splitext(canonical_file)[0])
with open("{0}_frac_paths_{1}_{2}.txt".format(output_prefix, base_n_file, base_c_file), 'w') as o:
for n, c_dict in novel_fracs.items():
for c, fracs_list in c_dict.items():
o.write("{0},{1},".format(n,c))
o.write(",".join(map(str,fracs_list)))
o.write("\n")
print "Output paths: {0}".format(datetime.datetime.now())
def main(sequence_list, trained_cleaved, trained_uncleaved):
sequences = seq_IO.read_sequences(sequence_list, additional_params=True)
trained_cleaved_list = seq_IO.read_sequences(trained_cleaved)
trained_uncleaved_list = seq_IO.read_sequences(trained_uncleaved)
base = os.path.splitext(sequence_list)[0]
cleaved_seqs = [ (s[0],s[1],min([conv.hamdist(s[0],c) for c in trained_cleaved_list])) for s in sequences if s[1] == 'CLEAVED' ]
uncleaved_seqs = [ (s[0],s[1],min([conv.hamdist(s[0],c) for c in trained_uncleaved_list])) for s in sequences if s[1] == 'UNCLEAVED' ]
outfile = '%s_selected_hamm.csv' % (base)
out = open(outfile,"w")
out.write("Cleaved_seqs\n")
out.write("\n".join( [ ",".join(map(str,s)) for s in cleaved_seqs ] ))
out.write("\nUncleaved_seqs\n")
out.write("\n".join( [ ",".join(map(str,s)) for s in uncleaved_seqs] ))
def main(list_sequence_names, canonical_list, output_prefix ):
series = []
canonical_list_seq = seq_IO.read_sequences(canonical_list)
cleaved_seqs = seq_IO.read_sequences( [ s for s,l in list_sequence_names if l == "CLEAVED" ][0] )
uncleaved_seqs = seq_IO.read_sequences( [ s for s,l in list_sequence_names if l == "UNCLEAVED" ][0] )
min_dist = []
avg_dist = []
max_dist = []
for seq in cleaved_seqs:
distances = [ conv.hamdist(seq, unc) for unc in uncleaved_seqs ]
min_dist.append(min(distances))
avg_dist.append(numpy.mean(distances))
max_dist.append(max(distances))
if seq in canonical_list_seq:
print seq
print min_dist[-1]
print avg_dist[-1]
print max_dist[-1]
fig, ax = pconv.create_ax(1, 3)
hist.draw_actual_plot(ax[0,0], min_dist, "Min. Distance from Boundary", "Minimum Distances", log=False, normed=True, label=None, nbins=15, stacked=False)
hist.draw_actual_plot(ax[1,0], avg_dist, "Avg. Distance from Boundary", "Average Distances", log=False, normed=True, label=None, nbins=15, stacked=False)
hist.draw_actual_plot(ax[2,0], max_dist, "Max. Distance from Boundary", "Maximum Distances", log=False, normed=True, label=None, nbins=15, stacked=False)
#ax[0,0].set_xlim(xmin=1,xmax=5)
#ax[0,0].set_xticks(xrange(1,6))
pconv.save_fig(fig, output_prefix, "dist_from_bounds", 18, 6, size=15)
def main(list_sequence_names, canonical_list, output_prefix, func_labels, unfunc_labels):
series = []
canonical_list_seq = seq_IO.read_sequences(canonical_list)
for canonical in canonical_list_seq:
dict_sequences = {}
for [filename, label] in list_sequence_names:
sequences = seq_IO.read_sequences(filename)
distances = [ conv.hamdist(seq, canonical) for seq in sequences ]
dict_sequences[label] = { i : sum([d for d in distances if d == i]) for i in xrange(1,6) }
x = []
y = []
for i in xrange(1,6):
func=0.0
unfunc=0.0
for label, dict_sums in dict_sequences.items():
if label in func_labels:
func = func + dict_sums[i]
elif label in unfunc_labels:
unfunc = unfunc + dict_sums[i]
if unfunc != 0:
x.append(i)
y.append( func/(func+unfunc) )
print x
print y
series.append([x, y, canonical])
fig, ax = pconv.create_ax(1, 1)
scatterplot.plot_series( ax[0,0], series, title="", x_axis="# of Mutations", y_axis="Fraction of Variants that are Functional", alpha=1.0, connect_dots=True, size=30, edgecolors='k')
ax[0,0].set_xlim(xmin=1,xmax=5)
ax[0,0].set_xticks(xrange(1,6))
pconv.save_fig(fig, output_prefix, canonical + "_fraction_func_mutant", 6, 6, size=15)
def main(input_dir, canonical_file, output_prefix, hamm_dist):
list_seq_files = glob.glob(os.path.join(input_dir, "*_cleaved.txt"))
dict_sequences = {}
canonical_sequences = []
canonical_sequences = seq_IO.read_sequences(canonical_file)
for filename in list_seq_files:
sequences = seq_IO.read_sequences(filename)
for can in canonical_sequences:
if hamm_dist == -1:
seq_sim = [ seq for seq in sequences if chem_sim(seq, can) ]
else:
seq_sim = [ seq for seq in sequences if conv.hamdist(seq,can) <= hamm_dist ]
if seq_sim:
dict_sequences[(filename, can)] = seq_sim
outfile_canon = '%scanonical_sim_cleaved%d.csv' % (output_prefix, hamm_dist)
canon_out = open(outfile_canon,"w")
for (filename, can), seqs in dict_sequences.items():
canon_out.write(filename + "," + can + "," + ','.join(seqs) + "\n")
def main(json_file, output_prefix, novel_seqs_file, canonical_file):
print "Started Script: {0}".format(datetime.datetime.now())
with open(json_file) as data_file:
data = json.load(data_file)
G = json_graph.node_link_graph(data, directed=False)
print "Finished Reading in Graph: {0}".format(datetime.datetime.now())
id_seq = networkx.get_node_attributes(G, "sequence")
id_status = networkx.get_node_attributes(G, "status")
seq_id = {seq: node_id for node_id, seq in id_seq.items()}
print "Created inverse lookup table: {0}".format(datetime.datetime.now())
novel_seqs = seq_IO.read_sequences(novel_seqs_file)
canonical_seqs = seq_IO.read_sequences(canonical_file)
novel_fracs = {}
print "Ready to enter loop: {0}".format(datetime.datetime.now())
for n in novel_seqs:
novel_fracs[n] = {}
hamm_dist = sorted([(conv.hamdist(n, c), c) for c in canonical_seqs])
min_hamm_dist = hamm_dist[0][0]
print "Found hamming distances: {0}".format(datetime.datetime.now())
for hamm, c in hamm_dist:
#only analyze min_dist canonical sequences
if hamm != min_hamm_dist:
continue
novel_fracs[n][c] = []
#generate list of 5 paths
#paths = itertools.islice(networkx.all_shortest_paths(G, seq_id[n], seq_id[c]), 5)
paths = [networkx.shortest_path(G, seq_id[n], seq_id[c])]
for path in paths:
inter_nodes = path[1:-1]
novel_fracs[n][c].append(
float(
sum([
1 for node_id in inter_nodes
if id_status[node_id] == "UNCLEAVED"
])) / len(inter_nodes))
base_n_file = os.path.basename(os.path.splitext(novel_seqs_file)[0])
base_c_file = os.path.basename(os.path.splitext(canonical_file)[0])
with open(
"{0}_frac_paths_{1}_{2}.txt".format(output_prefix, base_n_file,
base_c_file), 'w') as o:
for n, c_dict in novel_fracs.items():
for c, fracs_list in c_dict.items():
o.write("{0},{1},".format(n, c))
o.write(",".join(map(str, fracs_list)))
o.write("\n")
print "Output paths: {0}".format(datetime.datetime.now())
def main(list_sequence_names, hamming_dist, output_prefix, canonical_file):
list_sequences = [] #list of list of sequences, where each item represents a label
extended_list_sequences = [] #flat list of sequences
labels = [] #labels for list_sequences
#canonical_seqs = seq_IO.read_sequences(canonical_file)
canonical_seqs = ['DEMEE']
for [filename, label] in list_sequence_names:
sequences = seq_IO.read_sequences(filename, additional_params=True, ind_type={1:float})
new_seqs = [ (seq,fitness,min([ conv.hamdist(seq,can) for can in canonical_seqs ]) <= 2) for seq,fitness in sequences ]
list_sequences.append(new_seqs)
extended_list_sequences.extend(new_seqs[:])
labels.append(label)
outfile_nodes = '%s_nodes.csv' % (output_prefix)
edges = [(seq2,seq) for seq,seq2 in itertools.combinations(extended_list_sequences,2) if conv.hamdist(seq2[0],seq[0]) == hamming_dist ]
tallies = { 2 : {2:0,1.5:0,1:0}, 1.5 : {2:0,1.5:0,1:0}, 1 : {2:0,1.5:0,1:0} }
for edge in edges:
tallies[edge[0][1]][edge[1][1]] += 1
frequencies = { 2 : {}, 1.5 : {}, 1 : {} }
for source, tallies_dict in tallies.items():
n_tallies = float(sum(tallies_dict.values()))
frequencies[source] = { k : v/n_tallies for k, v in tallies_dict.items() }
new_edges = []
for edge in edges:
fitness_source = edge[0][1]
fitness_target = np.random.choice([2,1.5,1],p=[frequencies[fitness_source][2],frequencies[fitness_source][1.5],frequencies[fitness_source][1]])
seqs = list_sequences[labels.index(conv_fitness_label(fitness_target))]
new_edges.append((edge[0],seqs[np.random.randint(0,len(seqs)-1)]))
edges = new_edges
for canonical_seq in canonical_seqs:
outfile_edges = '%s_%s_edges.csv' % (output_prefix, canonical_seq)
edges_out = open(outfile_edges,"w")
edges_out.write("Source,Target,Weight\n")
print canonical_seq
for ([seq1,fit1,can1],[seq2,fit2,can2]) in edges:
dist_seq1 = conv.hamdist(canonical_seq, seq1)
dist_seq2 = conv.hamdist(canonical_seq, seq2)
fit_lower = fit1 if dist_seq1 < dist_seq2 else fit2
fit_upper = fit2 if dist_seq1 < dist_seq2 else fit1
fit_upper = fit_upper if fit_upper > 0 else 0.001
seq_lower = seq1 if dist_seq1 < dist_seq2 else seq2
seq_upper = seq2 if dist_seq1 < dist_seq2 else seq1
out_str = "{0},{1},{2}\n".format(seq_lower,seq_upper,fit_lower/float(fit_upper))
edges_out.write(out_str) #does this have the correct directionality?
edges_out.close()
already_written_nodes = []
nodes_out = open(outfile_nodes,"w")
nodes_out.write("Id,Label,Type,Fitness,Canonical\n")
for seqs,label in zip(list_sequences,labels):
nodes_out.write("\n".join("{0},{0},{1},{2},{3}".format(x, label, fitness,can) for (x,fitness,can) in seqs if x not in already_written_nodes))
already_written_nodes.extend([ s[0] for s in seqs])
nodes_out.write("\n")
def main(seq_file, canonical_file, output_prefix):
series = []
canonical_list_seq = seq_IO.read_sequences(canonical_file)
print "Beginning Script: {0}".format(datetime.datetime.now())
for canonical in canonical_list_seq:
with open(seq_file) as strings:
seq_list = strings.read().splitlines()
seq_ind_list = [(seq, ind) for ind, seq in enumerate(seq_list)]
orig_len = len(seq_ind_list)
if canonical not in seq_list:
one_away = gsconv.gen_hamdist_one(canonical)
one_away = [o for o in one_away if o != canonical] + [canonical]
seq_ind_list = seq_ind_list[:] + [
(o, ind) for (ind, o) in enumerate(one_away, len(seq_ind_list))
]
edges = [(seq2, seq)
for seq, seq2 in itertools.combinations(seq_ind_list, 2)
if gsconv.hamdist(seq2[0], seq[0]) < 2]
print len(seq_ind_list)
print "Generated Edges: {0}".format(datetime.datetime.now())
numpy.set_printoptions(threshold='nan')
canon_ind = [i for (s, i) in seq_ind_list if s == canonical][0]
T_mat = trans_matrix(seq_ind_list, edges)
#print raise_matrix(T_mat,1)
#print raise_matrix(T_mat,3)
#T = raise_matrix(T_mat,10)
#T = raise_matrix(T_mat,20)
x = [0]
y = [0]
print "Transformed Matrix: {0}".format(datetime.datetime.now())
x.append(1)
y.append(find_frac(T_mat, canon_ind, orig_len))
T_mat_new = T_mat
for i in range(2, 23):
x.append(i)
T_mat_new, frac = square_matrix(T_mat_new, T_mat, canon_ind,
orig_len)
y.append(frac)
print "Raised Matrix {0}: {1}".format(i, datetime.datetime.now())
series.append([x, y, canonical])
fig, ax = conv.create_ax(1, 1)
color = ['orange', 'palevioletred', 'mediumaquamarine', 'deepskyblue']
scatterplot.plot_series(ax[0, 0],
series,
title="",
x_axis="Number of Steps",
colors=color,
y_axis="Fraction Cleaved Variants Reached",
alpha=0.85,
connect_dots=True,
size=15,
edgecolors='k',
linewidth=0)
ax[0, 0].set_xlim(xmin=1)
ax[0, 0].set_ylim(ymin=0.0, ymax=1.0)
ax[0, 0].set_xticks(xrange(1, 23, 3))
lgd = conv.add_legend(ax[0, 0],
location='upper center',
bbox_to_anchor=(0.5, 1.05),
ncol=2,
size=8)
conv.save_fig(fig,
output_prefix,
"fraction_func",
2.5,
3,
size=9.5,
extra_artists=lgd)
print "Outputted Figure: {0}".format(datetime.datetime.now())
def main(seq_file, canonical_file, output_prefix):
series = []
canonical_list_seq = seq_IO.read_sequences(canonical_file)
print "Beginning Script: {0}".format(datetime.datetime.now())
for canonical in canonical_list_seq:
with open(seq_file) as strings:
seq_list = strings.read().splitlines()
seq_ind_list = [ (seq, ind) for ind, seq in enumerate(seq_list) ]
orig_len = len(seq_ind_list)
if canonical not in seq_list:
one_away = gsconv.gen_hamdist_one(canonical)
one_away = [ o for o in one_away if o != canonical ] + [canonical]
seq_ind_list = seq_ind_list[:] + [ (o, ind) for (ind, o) in enumerate(one_away, len(seq_ind_list)) ]
edges = [(seq2,seq) for seq,seq2 in itertools.combinations(seq_ind_list,2) if gsconv.hamdist(seq2[0],seq[0]) < 2 ]
print len(seq_ind_list)
print "Generated Edges: {0}".format(datetime.datetime.now())
numpy.set_printoptions(threshold='nan')
canon_ind=[ i for (s, i) in seq_ind_list if s == canonical ][0]
T_mat = trans_matrix(seq_ind_list,edges)
#print raise_matrix(T_mat,1)
#print raise_matrix(T_mat,3)
#T = raise_matrix(T_mat,10)
#T = raise_matrix(T_mat,20)
x = [0]
y = [0]
print "Transformed Matrix: {0}".format(datetime.datetime.now())
x.append(1)
y.append(find_frac(T_mat, canon_ind, orig_len))
T_mat_new = T_mat
for i in range(2,23):
x.append(i)
T_mat_new, frac = square_matrix(T_mat_new,T_mat,canon_ind, orig_len)
y.append(frac)
print "Raised Matrix {0}: {1}".format(i, datetime.datetime.now())
series.append([x,y,canonical])
fig, ax = conv.create_ax(1, 1)
color=['orange', 'palevioletred', 'mediumaquamarine', 'deepskyblue']
scatterplot.plot_series( ax[0,0], series, title="", x_axis="Number of Steps", colors=color, y_axis="Fraction Cleaved Variants Reached", alpha=0.85, connect_dots=True, size=15, edgecolors='k', linewidth=0)
ax[0,0].set_xlim(xmin=1)
ax[0,0].set_ylim(ymin=0.0, ymax=1.0)
ax[0,0].set_xticks(xrange(1,23,3))
lgd = conv.add_legend(ax[0,0], location='upper center', bbox_to_anchor=(0.5, 1.05), ncol=2, size=8)
conv.save_fig(fig, output_prefix, "fraction_func", 2.5, 3, size=9.5, extra_artists=lgd)
print "Outputted Figure: {0}".format(datetime.datetime.now())
def main(list_sequence_names, output_prefix):
list_sequences = [
] #list of list of sequences, where each item represents a label
extended_list_sequences = [] #flat list of sequences
labels = [] #labels for list_sequences
for [filename, label] in list_sequence_names:
sequences = seq_IO.read_sequences(filename,
additional_params=True,
ind_type={
1: float,
2: float
})
print sequences[0:10]
list_sequences.append(sequences)
extended_list_sequences.extend(sequences[:])
labels.append(label)
print len(extended_list_sequences)
dict_seq_fit = {
seq: fitness
for (seq, fitness, ratio) in extended_list_sequences
}
dict_seq_ratio = {
seq: ratio
for (seq, fitness, ratio) in extended_list_sequences
}
print len(dict_seq_fit)
epi = {}
outfile_epi = '%s_epi_double.csv' % (output_prefix)
epi_double_out = open(outfile_epi, "w")
outfile_epi = '%s_epi.csv' % (output_prefix)
epi_out = open(outfile_epi, "w")
mut_func = {
"Both_Functional": [],
"Both_Nonfunctional": [],
"One_Functional": []
}
mut_nonfunc = {
"Both_Functional": [],
"Both_Nonfunctional": [],
"One_Functional": []
}
prod = itertools.product(list_sequences[labels.index("CLEAVED")],
extended_list_sequences)
pairs = set()
counter = 0
counter_prod = 0
for x, y in prod:
counter_prod += 1
if x[0] != y[0]:
counter += 1
pairs.add(frozenset((x, y)))
print counter_prod
print len(pairs)
print counter
print "done making set"
for can, seq_fit in pairs:
canonical_seq = can[0]
seq = seq_fit[0]
fit = seq_fit[1]
mut_dict = mut_func if fit == 1 else mut_nonfunc
dist = conv.hamdist(canonical_seq, seq)
if dist <= 1:
continue
list_inter, list_fit = get_inter_fitness(canonical_seq, seq,
dict_seq_fit)
if None not in list_fit:
if dist == 2:
sum_fit = sum(list_fit)
if sum_fit > 1.95:
mut_dict["Both_Functional"].append(
(canonical_seq, seq, list_inter, list_fit))
elif sum_fit < 0.05:
mut_dict["Both_Nonfunctional"].append(
(canonical_seq, seq, list_inter, list_fit))
else: #either one uncleaved or one middle
mut_dict["One_Functional"].append(
(canonical_seq, seq, list_inter, list_fit))
epi[(canonical_seq, seq)] = (calc_epi(list_fit, fit), fit,
list_fit, list_inter)
print "done calc epi"
'''epi_double_out.write("Starting,Starting_Ratio,Ending,Ending_Ratio,Status_Ending,Status_Intermediates,Inter1_Seq,Inter1_Fit,Inter1_Ratio,Inter2_Seq,Inter2_Fit,Inter2_Ratio\n")
for label, list_muts in mut_func.items():
for (can, seq, list_inter, list_fit) in list_muts:
epi_double_out.write("{start},{start_ratio},{end},{end_ratio},End_Cleaved,{label},{data}\n".format(label=label,start=can,end=seq,
start_ratio=dict_seq_ratio[can],end_ratio=dict_seq_ratio[seq],
data = ",".join([ "{0},{1},{2}".format(seq,fitness_to_str(fit),dict_seq_ratio[seq]) for seq,fit in zip(list_inter,list_fit)])) )
for label, list_muts in mut_nonfunc.items():
for (can, seq, list_inter, list_fit) in list_muts:
epi_double_out.write("{start},{start_ratio},{end},{end_ratio},End_Uncleaved,{label},{data}\n".format(label=label,start=can,end=seq,
start_ratio=dict_seq_ratio[can],end_ratio=dict_seq_ratio[seq],
data = ",".join([ "{0},{1},{2}".format(seq,fit,dict_seq_ratio[seq]) for seq,fit in zip(list_inter,list_fit)])) )
'''
epi_out.write(
"Starting,Starting_Ratio,Ending,Ending_Ratio,Ending_Fitness,Epistasis,List_Seqs_Fitnesses_Ratios_Intermediates\n"
)
epi_out.write("\n".join([
"{0},{1},{2},{3},{4},{5},{6}".format(
can, dict_seq_ratio[can], seq, dict_seq_ratio[seq],
fitness_to_str(fit), e, ",".join([
"{0},{1},{2}".format(s, fitness_to_str(f), dict_seq_ratio[s])
for f, s in zip(list_fit, list_inter)
])) for (can, seq), (e, fit, list_fit, list_inter) in epi.items()
]))
epi_out.close()
epi_double_out.close()
print "done writing epi"
def main(list_sequence_names, canonical_list, output_prefix):
series = []
canonical_list_seq = seq_IO.read_sequences(canonical_list)
cleaved_seqs = seq_IO.read_sequences(
[s for s, l in list_sequence_names if l == "CLEAVED"][0])
uncleaved_seqs = seq_IO.read_sequences(
[s for s, l in list_sequence_names if l == "UNCLEAVED"][0])
min_dist = []
avg_dist = []
max_dist = []
for seq in cleaved_seqs:
distances = [conv.hamdist(seq, unc) for unc in uncleaved_seqs]
min_dist.append(min(distances))
avg_dist.append(numpy.mean(distances))
max_dist.append(max(distances))
if seq in canonical_list_seq:
print seq
print min_dist[-1]
print avg_dist[-1]
print max_dist[-1]
fig, ax = pconv.create_ax(1, 3)
hist.draw_actual_plot(ax[0, 0],
min_dist,
"Min. Distance from Boundary",
"Minimum Distances",
log=False,
normed=True,
label=None,
nbins=15,
stacked=False)
hist.draw_actual_plot(ax[1, 0],
avg_dist,
"Avg. Distance from Boundary",
"Average Distances",
log=False,
normed=True,
label=None,
nbins=15,
stacked=False)
hist.draw_actual_plot(ax[2, 0],
max_dist,
"Max. Distance from Boundary",
"Maximum Distances",
log=False,
normed=True,
label=None,
nbins=15,
stacked=False)
#ax[0,0].set_xlim(xmin=1,xmax=5)
#ax[0,0].set_xticks(xrange(1,6))
pconv.save_fig(fig, output_prefix, "dist_from_bounds", 18, 6, size=15)
def find_seqs_less_than(can, sequences, set_sequences, hamm_dist):
if hamm_dist == -1:
set_sequences = set_sequences.union([ seq for seq in sequences if chem_sim(seq, can) ])
else:
set_sequences = set_sequences.union([ seq for seq in sequences if conv.hamdist(seq,can) <= hamm_dist ])
return set_sequences
def find_seqs_more_than_first(can, sequences, set_sequences, hamm_dist):
if hamm_dist == -1:
set_sequences = set([ seq for seq in sequences if chem_sim(seq, can) ])
else:
set_sequences = set([ seq for seq in sequences if conv.hamdist(seq,can) > hamm_dist ])
return set_sequences
