def sim(self):
self.sim_root()
for edge in self.edge_list:
#print edge, self.edge_to_blen[edge]
# Now need to adapt branchsim
# create an instance for each branch
blen = self.edge_to_blen[edge]
num_exon = self.num_exon
x_exon = self.x_exon
x_IGC = deepcopy(self.x_IGC)
log_file = self.log_folder + '_'.join(edge) + '_log.log'
div_file = self.div_folder + '_'.join(edge) + '_div.log'
starting_seq = self.node_to_sequence[edge[0]]
if edge in self.outgroup:
x_IGC[0] = 0.0
self.OneBranchSimulator = OneBranchIGCCodonSimulator(
blen=blen,
num_exon=num_exon,
x_exon=x_exon,
x_IGC=x_IGC,
log_file=log_file,
div_file=div_file,
initial_seq=starting_seq)
blen = self.edge_to_blen[edge]
self.OneBranchSimulator.sim_one_branch(starting_seq, blen)
self.node_to_sequence[
edge[1]] = self.OneBranchSimulator.convert_list_to_seq()
self.total_mut += self.OneBranchSimulator.point_mut_count
self.total_IGC += self.OneBranchSimulator.IGC_total_count
self.total_IGC_sites += self.OneBranchSimulator.IGC_contribution_count
self.total_IGC_changes += self.OneBranchSimulator.IGC_change_sites
self.output_seq()
self.get_log()
self.write_log()
def sim(self):
self.sim_root()
for edge in self.edge_list:
# print edge, self.edge_to_blen[edge]
# Now need to adapt branchsim
# create an instance for each branch
blen = self.edge_to_blen[edge]
num_exon = self.num_exon
x_exon = self.x_exon
x_IGC = deepcopy(self.x_IGC)
log_file = self.log_folder + "_".join(edge) + "_log.log"
div_file = self.div_folder + "_".join(edge) + "_div.log"
starting_seq = self.node_to_sequence[edge[0]]
if edge in self.outgroup:
x_IGC[0] = 0.0
self.OneBranchSimulator = OneBranchIGCCodonSimulator(
blen=blen,
num_exon=num_exon,
x_exon=x_exon,
x_IGC=x_IGC,
log_file=log_file,
div_file=div_file,
initial_seq=starting_seq,
)
blen = self.edge_to_blen[edge]
self.OneBranchSimulator.sim_one_branch(starting_seq, blen)
self.node_to_sequence[edge[1]] = self.OneBranchSimulator.convert_list_to_seq()
self.total_mut += self.OneBranchSimulator.point_mut_count
self.total_IGC += self.OneBranchSimulator.IGC_total_count
self.total_IGC_sites += self.OneBranchSimulator.IGC_contribution_count
self.total_IGC_changes += self.OneBranchSimulator.IGC_change_sites
self.output_seq()
self.get_log()
self.write_log()
class TreeIGCCodonSimulator:
def __init__(self, num_exon, newick_tree, paralog, seq_file, log_file, x_exon, x_IGC, log_folder, div_folder):
self.newicktree = newick_tree
self.num_exon = num_exon
self.pair = paralog
self.seq_file = seq_file
self.log_file = log_file
self.edge_to_blen = None
self.node_to_num = None
self.num_to_node = None
self.edge_list = None
self.outgroup = [("N0", "kluyveri")] # I am so lazy
# Node sequence
self.node_to_sequence = None
self.node_to_sim = {}
# OneBranchIGCSimulator Related Parameters
self.x_exon = x_exon # parameter values for exon model
self.x_IGC = x_IGC
self.Model = "MG94"
self.num_paralog = 2
self.log_folder = log_folder
self.div_folder = div_folder
self.distn = None
self.mut_Q = None
self.OneBranchSimulator = None
# Constants for Sequence operations
bases = "tcag".upper()
codons = [a + b + c for a in bases for b in bases for c in bases]
amino_acids = "FFLLSSSSYY**CC*WLLLLPPPPHHQQRRRRIIIMTTTTNNKKSSRRVVVVAAAADDEEGGGG"
self.nt_to_state = {a: i for (i, a) in enumerate("ACGT")}
self.state_to_nt = {i: a for (i, a) in enumerate("ACGT")}
self.codon_table = dict(zip(codons, amino_acids))
self.codon_nonstop = [a for a in self.codon_table.keys() if not self.codon_table[a] == "*"]
self.codon_to_state = {a.upper(): i for (i, a) in enumerate(self.codon_nonstop)}
self.state_to_codon = {i: a.upper() for (i, a) in enumerate(self.codon_nonstop)}
if self.Model == "MG94":
self.pair_to_state = {pair: i for i, pair in enumerate(product(self.codon_nonstop, repeat=2))}
self.state_to_pair = {self.pair_to_state[pair]: pair for pair in self.pair_to_state}
# Total event counts
self.total_mut = 0 # Total mutation events
self.total_IGC = 0 # Total IGC events
self.total_IGC_sites = 0 # Total IGC affecting sites
self.total_IGC_changes = 0 # Total changes due to IGC rather than point mutation
self.initiate()
def initiate(self):
self.get_tree()
self.unpack_x()
# If the seed file exists, set numpy's random seed state according to the seed file
seed_file = self.log_file.replace(".log", "_seed.log")
if os.path.isfile(seed_file):
prng = cPickle.load(open(seed_file, "r"))
np.random.set_state(prng.get_state())
else:
prng = np.random.RandomState()
seed_file = self.log_file.replace(".log", "_seed.log")
cPickle.dump(prng, open(seed_file, "w+"))
def unpack_x(self):
if self.Model == "MG94":
# get stationary nucleotide distribution of codon model of exonic region
pi_codon = self.unpack_x_exon()[0]
distn_codon = [reduce(mul, [pi_codon["ACGT".index(b)] for b in codon], 1) for codon in self.codon_nonstop]
distn_codon = np.array(distn_codon) / sum(distn_codon)
self.distn = distn_codon
self.mut_Q = self.get_MG94()
self.node_to_sequence = {node: [] for node in self.node_to_num.keys()}
def unpack_x_exon(self, log=False):
if log:
x_exon = np.exp(self.x_exon)
else:
x_exon = self.x_exon
if self.Model == "MG94":
assert len(self.x_exon) == 5
# %AG, %A, %C, kappa, omega
pi_a = x_exon[0] * x_exon[1]
pi_c = (1 - x_exon[0]) * x_exon[2]
pi_g = x_exon[0] * (1 - x_exon[1])
pi_t = (1 - x_exon[0]) * (1 - x_exon[2])
pi = [pi_a, pi_c, pi_g, pi_t]
kappa = x_exon[3]
omega = x_exon[4]
return [pi, kappa, omega]
def get_MG94(self):
Qbasic = np.zeros((61, 61), dtype=float)
pi, kappa, omega = self.unpack_x_exon()
for ca in self.codon_nonstop:
for cb in self.codon_nonstop:
if ca == cb:
continue
Qbasic[self.codon_to_state[ca], self.codon_to_state[cb]] = get_MG94BasicRate(
ca, cb, pi, kappa, omega, self.codon_table
)
expected_rate = np.dot(self.distn, Qbasic.sum(axis=1))
Qbasic = Qbasic / expected_rate
return Qbasic
def get_tree(self):
tree = Phylo.read(self.newicktree, "newick")
# set node number for nonterminal nodes and specify root node
numInternalNode = 0
for clade in tree.get_nonterminals():
clade.name = "N" + str(numInternalNode)
numInternalNode += 1
tree_phy = tree.as_phyloxml(rooted="True")
tree_nx = Phylo.to_networkx(tree_phy)
triples = (
(u.name, v.name, d["weight"]) for (u, v, d) in tree_nx.edges(data=True)
) # data = True to have the blen as 'weight'
T = nx.DiGraph()
edge_to_blen = {}
for va, vb, blen in triples:
edge = (va, vb)
T.add_edge(*edge)
edge_to_blen[edge] = blen
self.edge_to_blen = edge_to_blen
# Now assign node_to_num
leaves = set(v for v, degree in T.degree().items() if degree == 1)
self.leaves = list(leaves)
internal_nodes = set(list(T)).difference(leaves)
node_names = list(internal_nodes) + list(leaves)
self.node_to_num = {n: i for i, n in enumerate(node_names)}
self.num_to_node = {self.node_to_num[i]: i for i in self.node_to_num}
# Prepare for generating self.tree so that it has same order as the self.x_process
nEdge = len(self.edge_to_blen) # number of edges
l = nEdge / 2 + 1 # number of leaves
k = (
l - 1
) # number of internal nodes. The notation here is inconsistent with Alex's for trying to match my notes.
leaf_branch = [
edge
for edge in self.edge_to_blen.keys()
if edge[0][0] == "N" and str.isdigit(edge[0][1:]) and not str.isdigit(edge[1][1:])
]
out_group_branch = [edge for edge in leaf_branch if edge[0] == "N0" and not str.isdigit(edge[1][1:])][0]
internal_branch = [x for x in self.edge_to_blen.keys() if not x in leaf_branch]
assert (
len(internal_branch) == k - 1
) # check if number of internal branch is one less than number of internal nodes
leaf_branch.sort(
key=lambda node: int(node[0][1:])
) # sort the list by the first node number in increasing order
internal_branch.sort(
key=lambda node: int(node[0][1:])
) # sort the list by the first node number in increasing order
edge_list = []
for i in range(len(internal_branch)):
edge_list.append(internal_branch[i])
edge_list.append(leaf_branch[i])
for j in range(len(leaf_branch[i + 1 :])):
edge_list.append(leaf_branch[i + 1 + j])
self.edge_list = edge_list
def unpack_x_rates(self, x_rates):
for edge_iter in range(len(self.edge_list)):
edge = self.edge_list[edge_iter]
self.edge_to_blen[edge] = x_rates[edge_iter]
def sim(self):
self.sim_root()
for edge in self.edge_list:
# print edge, self.edge_to_blen[edge]
# Now need to adapt branchsim
# create an instance for each branch
blen = self.edge_to_blen[edge]
num_exon = self.num_exon
x_exon = self.x_exon
x_IGC = deepcopy(self.x_IGC)
log_file = self.log_folder + "_".join(edge) + "_log.log"
div_file = self.div_folder + "_".join(edge) + "_div.log"
starting_seq = self.node_to_sequence[edge[0]]
if edge in self.outgroup:
x_IGC[0] = 0.0
self.OneBranchSimulator = OneBranchIGCCodonSimulator(
blen=blen,
num_exon=num_exon,
x_exon=x_exon,
x_IGC=x_IGC,
log_file=log_file,
div_file=div_file,
initial_seq=starting_seq,
)
blen = self.edge_to_blen[edge]
self.OneBranchSimulator.sim_one_branch(starting_seq, blen)
self.node_to_sequence[edge[1]] = self.OneBranchSimulator.convert_list_to_seq()
self.total_mut += self.OneBranchSimulator.point_mut_count
self.total_IGC += self.OneBranchSimulator.IGC_total_count
self.total_IGC_sites += self.OneBranchSimulator.IGC_contribution_count
self.total_IGC_changes += self.OneBranchSimulator.IGC_change_sites
self.output_seq()
self.get_log()
self.write_log()
def sim_root(self):
if self.Model == "MG94":
seq = draw_from_distribution(self.distn, self.num_exon, self.codon_nonstop)
# self.node_to_sequence['N0'] = np.array([self.pair_to_state[(i, i)] for i in seq])
self.node_to_sequence["N0"] = ["".join(seq), "".join(seq)]
self.node_to_sim["N0"] = [self.node_to_sequence["N0"], 0, 0]
def output_seq(self):
with open(self.seq_file, "w+") as f:
for node in self.leaves:
if not node in self.outgroup[0]:
for paralog_counter in range(self.num_paralog):
paralog = self.pair[paralog_counter]
f.write(">" + node + paralog + "\n")
f.write(self.node_to_sequence[node][paralog_counter] + "\n")
else: # only observe one paralog of the outgroup species
paralog = self.pair[0]
f.write(">" + node + paralog + "\n")
f.write(self.node_to_sequence[node][paralog_counter] + "\n")
def get_log(self):
with open(self.log_file, "w+") as f:
f.write("Model: " + self.Model + " nSites: " + str(self.num_exon) + " TreeFile: " + self.newicktree + "\n")
f.write("x_exon: " + ", ".join([str(parameter) for parameter in self.x_exon]) + "\n")
f.write("x_IGC: " + ", ".join([str(parameter) for parameter in self.x_IGC]) + "\n")
f.write("\n".join(["_".join(edge) + ": " + str(self.edge_to_blen[edge]) for edge in self.edge_list]) + "\n")
f.write(
"Total point mutation: "
+ str(self.total_mut)
+ " Total IGC: "
+ str(self.total_IGC)
+ " Total IGC affecting sites: "
+ str(self.total_IGC_sites)
+ " Total changes due to IGC: "
+ str(self.total_IGC_changes)
+ "\n"
)
f.write(
"% change due to IGC: "
+ str((self.total_IGC_changes + 0.0) / (self.total_mut + self.total_IGC_changes + 0.0))
+ "\n"
)
def write_log(self):
label = ["%IGC", "#mut", "#IGC"]
summary = np.matrix(
[
(self.total_IGC_changes + 0.0) / (self.total_mut + self.total_IGC_changes + 0.0),
self.total_mut,
self.total_IGC,
]
)
short_log_file = self.log_file.replace(".log", "_short.log")
footer = " ".join(label)
np.savetxt(open(short_log_file, "w+"), summary.T, delimiter=" ", footer=footer)
class TreeIGCCodonSimulator:
def __init__(self, num_exon, newick_tree, paralog, seq_file, log_file,
x_exon, x_IGC, log_folder, div_folder, seed_number):
self.newicktree = newick_tree
self.num_exon = num_exon
self.pair = paralog
self.seq_file = seq_file
self.log_file = log_file
self.seed_number = seed_number
self.edge_to_blen = None
self.node_to_num = None
self.num_to_node = None
self.edge_list = None
self.outgroup = [('N0', 'kluyveri')] # I am so lazy
# Node sequence
self.node_to_sequence = None
self.node_to_sim = {}
# OneBranchIGCSimulator Related Parameters
self.x_exon = x_exon # parameter values for exon model
self.x_IGC = x_IGC
self.Model = 'MG94'
self.num_paralog = 2
self.log_folder = log_folder
self.div_folder = div_folder
self.distn = None
self.mut_Q = None
self.OneBranchSimulator = None
# Constants for Sequence operations
bases = 'tcag'.upper()
codons = [a + b + c for a in bases for b in bases for c in bases]
amino_acids = 'FFLLSSSSYY**CC*WLLLLPPPPHHQQRRRRIIIMTTTTNNKKSSRRVVVVAAAADDEEGGGG'
self.nt_to_state = {a: i for (i, a) in enumerate('ACGT')}
self.state_to_nt = {i: a for (i, a) in enumerate('ACGT')}
self.codon_table = dict(zip(codons, amino_acids))
self.codon_nonstop = [
a for a in self.codon_table.keys()
if not self.codon_table[a] == '*'
]
self.codon_to_state = {
a.upper(): i
for (i, a) in enumerate(self.codon_nonstop)
}
self.state_to_codon = {
i: a.upper()
for (i, a) in enumerate(self.codon_nonstop)
}
if self.Model == 'MG94':
self.pair_to_state = {
pair: i
for i, pair in enumerate(product(self.codon_nonstop, repeat=2))
}
self.state_to_pair = {
self.pair_to_state[pair]: pair
for pair in self.pair_to_state
}
# Total event counts
self.total_mut = 0 # Total mutation events
self.total_IGC = 0 # Total IGC events
self.total_IGC_sites = 0 # Total IGC affecting sites
self.total_IGC_changes = 0 # Total changes due to IGC rather than point mutation
self.initiate()
def initiate(self):
self.get_tree()
self.unpack_x()
self.set_seed()
## # If the seed file exists, set numpy's random seed state according to the seed file
## seed_file = self.log_file.replace('.log', '_seed.log')
## if os.path.isfile(seed_file):
## prng = cPickle.load(open(seed_file, 'r'))
## np.random.set_state(prng.get_state())
## else:
## prng = np.random.RandomState()
## seed_file = self.log_file.replace('.log', '_seed.log')
## cPickle.dump(prng, open(seed_file, 'w+'))
def set_seed(self):
assert (type(self.seed_number) == int)
np.random.seed(self.seed_number)
def unpack_x(self):
if self.Model == 'MG94':
# get stationary nucleotide distribution of codon model of exonic region
pi_codon = self.unpack_x_exon()[0]
distn_codon = [
reduce(mul, [pi_codon['ACGT'.index(b)] for b in codon], 1)
for codon in self.codon_nonstop
]
distn_codon = np.array(distn_codon) / sum(distn_codon)
self.distn = distn_codon
self.mut_Q = self.get_MG94()
self.node_to_sequence = {node: [] for node in self.node_to_num.keys()}
def unpack_x_exon(self, log=False):
if log:
x_exon = np.exp(self.x_exon)
else:
x_exon = self.x_exon
if self.Model == 'MG94':
assert (len(self.x_exon) == 5)
# %AG, %A, %C, kappa, omega
pi_a = x_exon[0] * x_exon[1]
pi_c = (1 - x_exon[0]) * x_exon[2]
pi_g = x_exon[0] * (1 - x_exon[1])
pi_t = (1 - x_exon[0]) * (1 - x_exon[2])
pi = [pi_a, pi_c, pi_g, pi_t]
kappa = x_exon[3]
omega = x_exon[4]
return [pi, kappa, omega]
def get_MG94(self):
Qbasic = np.zeros((61, 61), dtype=float)
pi, kappa, omega = self.unpack_x_exon()
for ca in self.codon_nonstop:
for cb in self.codon_nonstop:
if ca == cb:
continue
Qbasic[self.codon_to_state[ca],
self.codon_to_state[cb]] = get_MG94BasicRate(
ca, cb, pi, kappa, omega, self.codon_table)
expected_rate = np.dot(self.distn, Qbasic.sum(axis=1))
Qbasic = Qbasic / expected_rate
return Qbasic
def get_tree(self):
tree = Phylo.read(self.newicktree, "newick")
#set node number for nonterminal nodes and specify root node
numInternalNode = 0
for clade in tree.get_nonterminals():
clade.name = 'N' + str(numInternalNode)
numInternalNode += 1
tree_phy = tree.as_phyloxml(rooted='True')
tree_nx = Phylo.to_networkx(tree_phy)
triples = ((u.name, v.name, d['weight'])
for (u, v, d) in tree_nx.edges(data=True)
) # data = True to have the blen as 'weight'
T = nx.DiGraph()
edge_to_blen = {}
for va, vb, blen in triples:
edge = (va, vb)
T.add_edge(*edge)
edge_to_blen[edge] = blen
self.edge_to_blen = edge_to_blen
# Now assign node_to_num
leaves = set(v for v, degree in T.degree().items() if degree == 1)
self.leaves = list(leaves)
internal_nodes = set(list(T)).difference(leaves)
node_names = list(internal_nodes) + list(leaves)
self.node_to_num = {n: i for i, n in enumerate(node_names)}
self.num_to_node = {self.node_to_num[i]: i for i in self.node_to_num}
# Prepare for generating self.tree so that it has same order as the self.x_process
nEdge = len(self.edge_to_blen) # number of edges
l = nEdge / 2 + 1 # number of leaves
k = l - 1 # number of internal nodes. The notation here is inconsistent with Alex's for trying to match my notes.
leaf_branch = [
edge for edge in self.edge_to_blen.keys() if edge[0][0] == 'N'
and str.isdigit(edge[0][1:]) and not str.isdigit(edge[1][1:])
]
out_group_branch = [
edge for edge in leaf_branch
if edge[0] == 'N0' and not str.isdigit(edge[1][1:])
][0]
internal_branch = [
x for x in self.edge_to_blen.keys() if not x in leaf_branch
]
assert (
len(internal_branch) == k - 1
) # check if number of internal branch is one less than number of internal nodes
leaf_branch.sort(key=lambda node: int(node[0][
1:])) # sort the list by the first node number in increasing order
internal_branch.sort(key=lambda node: int(node[0][
1:])) # sort the list by the first node number in increasing order
edge_list = []
for i in range(len(internal_branch)):
edge_list.append(internal_branch[i])
edge_list.append(leaf_branch[i])
for j in range(len(leaf_branch[i + 1:])):
edge_list.append(leaf_branch[i + 1 + j])
self.edge_list = edge_list
def unpack_x_rates(self, x_rates):
for edge_iter in range(len(self.edge_list)):
edge = self.edge_list[edge_iter]
self.edge_to_blen[edge] = x_rates[edge_iter]
def sim(self):
self.sim_root()
for edge in self.edge_list:
#print edge, self.edge_to_blen[edge]
# Now need to adapt branchsim
# create an instance for each branch
blen = self.edge_to_blen[edge]
num_exon = self.num_exon
x_exon = self.x_exon
x_IGC = deepcopy(self.x_IGC)
log_file = self.log_folder + '_'.join(edge) + '_log.log'
div_file = self.div_folder + '_'.join(edge) + '_div.log'
starting_seq = self.node_to_sequence[edge[0]]
if edge in self.outgroup:
x_IGC[0] = 0.0
self.OneBranchSimulator = OneBranchIGCCodonSimulator(
blen=blen,
num_exon=num_exon,
x_exon=x_exon,
x_IGC=x_IGC,
log_file=log_file,
div_file=div_file,
initial_seq=starting_seq)
blen = self.edge_to_blen[edge]
self.OneBranchSimulator.sim_one_branch(starting_seq, blen)
self.node_to_sequence[
edge[1]] = self.OneBranchSimulator.convert_list_to_seq()
self.total_mut += self.OneBranchSimulator.point_mut_count
self.total_IGC += self.OneBranchSimulator.IGC_total_count
self.total_IGC_sites += self.OneBranchSimulator.IGC_contribution_count
self.total_IGC_changes += self.OneBranchSimulator.IGC_change_sites
self.output_seq()
self.get_log()
self.write_log()
def sim_root(self):
if self.Model == 'MG94':
seq = draw_from_distribution(self.distn, self.num_exon,
self.codon_nonstop)
#self.node_to_sequence['N0'] = np.array([self.pair_to_state[(i, i)] for i in seq])
self.node_to_sequence['N0'] = [''.join(seq), ''.join(seq)]
self.node_to_sim['N0'] = [self.node_to_sequence['N0'], 0, 0]
def output_seq(self):
with open(self.seq_file, 'w+') as f:
for node in self.node_to_sequence:
if not node in self.outgroup[0]:
for paralog_counter in range(self.num_paralog):
paralog = self.pair[paralog_counter]
f.write('>' + node + paralog + '\n')
f.write(self.node_to_sequence[node][paralog_counter] +
'\n')
else: # only observe one paralog of the outgroup species
paralog = self.pair[0]
f.write('>' + node + paralog + '\n')
f.write(self.node_to_sequence[node][paralog_counter] +
'\n')
def get_log(self):
with open(self.log_file, 'w+') as f:
f.write('Model: ' + self.Model + ' nSites: ' +
str(self.num_exon) + ' TreeFile: ' + self.newicktree +
'\n')
f.write('x_exon: ' +
', '.join([str(parameter)
for parameter in self.x_exon]) + '\n')
f.write('x_IGC: ' +
', '.join([str(parameter)
for parameter in self.x_IGC]) + '\n')
f.write('\n'.join([
'_'.join(edge) + ': ' + str(self.edge_to_blen[edge])
for edge in self.edge_list
]) + '\n')
f.write('Total point mutation: ' + str(self.total_mut) +
' Total IGC: ' + str(self.total_IGC) +
' Total IGC affecting sites: ' +
str(self.total_IGC_sites) + ' Total changes due to IGC: ' +
str(self.total_IGC_changes) + '\n')
f.write('% change due to IGC: ' +
str((self.total_IGC_changes + 0.0) /
(self.total_mut + self.total_IGC_changes + 0.0)) +
'\n')
def write_log(self):
label = ['%IGC', '#mut', '#IGC']
summary = np.matrix([(self.total_IGC_changes + 0.0) /
(self.total_mut + self.total_IGC_changes + 0.0),
self.total_mut, self.total_IGC])
short_log_file = self.log_file.replace('.log', '_short.log')
footer = ' '.join(label)
np.savetxt(open(short_log_file, 'w+'),
summary.T,
delimiter=' ',
footer=footer)
