def invFels(y, x, L=1000, alphabet='nuc_nogap'):
from io import StringIO
T = Phylo.read(StringIO("(A:%f,B:%f,(C:%f,D:%f):%f);" % (y, y, x, x, y)),
"newick")
gtr = GTR(alphabet=alphabet)
gtr.seq_len = L
mySeq = SeqGen(gtr=gtr, tree=T)
mySeq.evolve()
return T, mySeq.get_aln()
def infer_gene_gain_loss(rates = [1.0, 1.0], path_to_pangenome_dir = '/ebio/abt6_projects9/Pseudomonas_diversity/data/post_assembly_analysis/pan_genome/Ta1524/'):
'''code nabbed and edited from panX'''
# initialize GTR model with default parameters
mu = np.sum(rates)
gene_pi = np.array(rates)/mu
gain_loss_model = GTR.custom(pi = gene_pi, mu=mu, W=np.ones((2,2)), alphabet = np.array(['0','1']))
# add "unknown" state to profile
gain_loss_model.profile_map['-'] = np.ones(2)
#root_dir = os.path.dirname(os.path.realpath(__file__))
# define file names for pseudo alignment of presence/absence patterns as in 001001010110
#path_to_pangenome_dir='/ebio/ag-neher/share/users/wding/panX-refseq/data/Pseudomonadales'#sys.argv[1]
nwk=path_to_pangenome_dir+"/vis/strain_tree.nwk"
fasta=path_to_pangenome_dir+"/geneCluster/genePresence.aln"
# instantiate treetime with custom GTR
t = ta.TreeAnc(nwk, gtr =gain_loss_model, verbose=2)
# fix leaves names since Bio.Phylo interprets numeric leaf names as confidence
for leaf in t.tree.get_terminals():
if leaf.name is None:
leaf.name = str(leaf.confidence)
t.aln = fasta
t.tree.root.branch_length=0.0001
t.reconstruct_anc(method='ml')
for n in t.tree.find_clades():
n.genepresence = n.sequence
return t
def infer_gene_gain_loss(path, rates=[1.0, 1.0]):
# initialize GTR model with default parameters
mu = np.sum(rates)
gene_pi = np.array(rates) / mu
gain_loss_model = GTR.custom(pi=gene_pi,
mu=mu,
W=np.ones((2, 2)),
alphabet=np.array(['0', '1']))
# add "unknown" state to profile
gain_loss_model.profile_map['-'] = np.ones(2)
root_dir = os.path.dirname(os.path.realpath(__file__))
# define file names for pseudo alignment of presence/absence patterns as in 001001010110
sep = '/'
fasta = sep.join([path.rstrip(sep), 'geneCluster', 'genePresence.aln'])
# strain tree based on core gene SNPs
nwk = sep.join([path.rstrip(sep), 'geneCluster', 'strain_tree.nwk'])
# instantiate treetime with custom GTR
t = ta.TreeAnc(nwk, gtr=gain_loss_model, verbose=2)
# fix leaves names since Bio.Phylo interprets numeric leaf names as confidence
for leaf in t.tree.get_terminals():
if leaf.name is None:
leaf.name = str(leaf.confidence)
t.aln = fasta
t.tree.root.branch_length = 0.0001
t.reconstruct_anc(method='ml')
for n in t.tree.find_clades():
n.genepresence = n.sequence
return t
def geo_inference(self, attr):
from treetime.gtr import GTR
places = set()
for node in self.tree.find_clades():
if hasattr(node, attr):
places.add(node.__getattribute__(attr))
if hasattr(node, 'sequence'):
node.nuc_sequence = node.sequence
places = sorted(places)
alphabet = {chr(65+i):place for i,place in enumerate(places)}
alphabet_rev = {v:k for k,v in alphabet.iteritems()}
self.tt.sequence_gtr = self.tt.gtr
nc = len(places)
myGeoGTR = GTR.custom(pi = np.ones(nc, dtype=float)/nc, W=np.ones((nc,nc)),
alphabet = np.array(sorted(alphabet.keys())))
myGeoGTR.profile_map['-'] = np.ones(nc)
for node in self.tree.get_terminals():
if hasattr(node, attr):
node.sequence=np.array([alphabet_rev[node.__getattribute__(attr)]])
else:
node.sequence=np.array(['-'])
for node in self.tree.get_nonterminals():
node.__delattr__('sequence')
self.tt._gtr = myGeoGTR
self.tt.reconstruct_anc(method='ml')
self.tt.infer_gtr(print_raw=True)
self.tt.reconstruct_anc(method='ml')
self.tt.geogtr = self.tt.gtr
self.tt._gtr = self.tt.sequence_gtr
for node in self.tree.find_clades():
node.__setattr__(attr, alphabet[node.sequence[0]])
node.sequence = node.nuc_sequence
def tt_from_file(self, infile, root='none'):
from treetime.gtr import GTR
from treetime import io, utils
gtr = GTR.standard()
self.tt = io.treetime_from_newick(gtr, infile)
io.set_seqs_to_leaves(self.tt, self.aln)
io.set_node_dates_from_dic(self.tt, {seq.id:utils.numeric_date(seq.attributes['date'])
for seq in self.aln if 'date' in seq.attributes})
self.tree = self.tt.tree
if root=='midpoint':
self.tt.tree.root_at_midpoint()
self.tt.set_additional_tree_params()
elif root=='oldest':
tmp = self.tt.reroot_to_oldest()
for node in self.tree.get_terminals():
if node.name in self.sequence_lookup:
seq = self.sequence_lookup[node.name]
for attr in seq.attributes:
if attr == 'date':
node.date = seq.attributes['date'].strftime('%Y-%m-%d')
else:
node.__setattr__(attr, seq.attributes[attr])
from __future__ import print_function, division
import numpy as np
from Bio import Phylo
if __name__ == '__main__':
from treetime.seq_utils import normalize_profile, prof2seq, seq2prof
from treetime.gtr import GTR
gtr = GTR.standard('JC69')
dummy_prof = np.random.random(size=(10000, 5))
# used a lot (300us)
norm_prof = normalize_profile(dummy_prof)[0]
# used less but still a lot (50us)
gtr.evolve(norm_prof, 0.1)
# used less but still a lot (50us)
gtr.propagate_profile(norm_prof, 0.1)
# used only in final, sample_from_prof=False speeds it up (600us or 300us)
seq, p, seq_ii = prof2seq(norm_prof,
gtr,
sample_from_prof=True,
normalize=False)
# used only initially (slow, 5ms)
tmp_prof = seq2prof(seq, gtr.profile_map)
def date_from_seq_name(name):
date = str2date_time(name.split('|')[2].strip())
return date.year + date.timetuple().tm_yday / 365.25
if __name__=='__main__':
root_dir = os.path.dirname(os.path.realpath(__file__))
file_base = '../data/H3N2_NA_allyears_NA.200'
fasta = os.path.join(root_dir, file_base+'.fasta')
nwk = os.path.join(root_dir, file_base+'.nwk')
mdf = os.path.join(root_dir, file_base+'.metadata.csv')
# read tree from file
gtr = GTR.standard()
t = io.treetime_from_newick(gtr, nwk)
# set alignment to the tree
io.set_seqs_to_leaves(t, AlignIO.read(fasta, 'fasta'))
io.read_metadata(t, mdf)
t.reroot_to_best_root(infer_gtr=True)
t.init_date_constraints()
t.ml_t()
# plotting the results
t._score_branches()
t.tree.ladderize()
if polytomies:
#Phylo.draw(t.tree, label_func = lambda x:'', show_confidence=False, branch_labels='')
t1 = copy.deepcopy(t)
t1.resolve_polytomies()
def date_from_seq_name(name):
date = str2date_time(name.split('|')[2].strip())
return date.year + date.timetuple().tm_yday / 365.25
if __name__ == '__main__':
root_dir = os.path.dirname(os.path.realpath(__file__))
fasta = os.path.join(root_dir, '../data/H3N2_NA_allyears_NA.20.fasta')
nwk = os.path.join(root_dir, '../data/H3N2_NA_allyears_NA.20.nwk')
mdf = os.path.join(root_dir, '../data/H3N2_NA_allyears_NA.20.metadata.csv')
# read tree from file
gtr = GTR.standard()
t = io.treetime_from_newick(gtr, nwk)
# set alignment to the tree
io.set_seqs_to_leaves(t, AlignIO.read(fasta, 'fasta'))
io.read_metadata(t, mdf)
t.reroot_to_best_root(infer_gtr=True)
t.init_date_constraints()
t.ml_t()
# plotting the results
t._score_branches()
t.tree.ladderize()
#Phylo.draw(t.tree, label_func = lambda x:'', show_confidence=False, branch_labels='')
t1 = copy.deepcopy(t)
t1.resolve_polytomies()
t1.tree.ladderize()
def geo_inference(self, attr):
'''
infer a "mugration" model by pretending each region corresponds to a sequence
state and repurposing the GTR inference and ancestral reconstruction
'''
from treetime.gtr import GTR
# Determine alphabet and store reconstructed ancestral sequences
places = set()
nuc_seqs = {}
nuc_muts = {}
nuc_seq_LH = None
if hasattr(self.tt.tree, 'sequence_LH'):
nuc_seq_LH = self.tt.tree.sequence_LH
for node in self.tree.find_clades():
if hasattr(node, 'attr'):
if attr in node.attr:
places.add(node.attr[attr])
if hasattr(node, 'sequence'):
nuc_seqs[node] = node.sequence
if hasattr(node, 'mutations'):
nuc_muts[node] = node.mutations
node.__delattr__('mutations')
# construct GTR (flat for now). The missing DATA symbol is a '-' (ord('-')==45)
places = sorted(places)
nc = len(places)
if nc < 2 or nc > 180:
print(
"geo_inference: can't have less than 2 or more than 180 places!"
)
return
alphabet = {chr(65 + i): place for i, place in enumerate(places)}
alphabet_rev = {v: k for k, v in alphabet.iteritems()}
sequence_gtr = self.tt.gtr
myGeoGTR = GTR.custom(pi=np.ones(nc, dtype=float) / nc,
W=np.ones((nc, nc)),
alphabet=np.array(sorted(alphabet.keys())))
myGeoGTR.profile_map['-'] = np.ones(nc)
# set geo info to nodes as one letter sequence.
for node in self.tree.get_terminals():
if hasattr(node, 'attr'):
if attr in node.attr:
node.sequence = np.array([alphabet_rev[node.attr[attr]]])
else:
node.sequence = np.array(['-'])
for node in self.tree.get_nonterminals():
node.__delattr__('sequence')
# set custom GTR model, run inference
self.tt._gtr = myGeoGTR
tmp_use_mutation_length = self.tt.use_mutation_length
self.tt.use_mutation_length = False
self.tt.infer_ancestral_sequences(method='ml',
infer_gtr=True,
store_compressed=False,
pc=5.0,
marginal=True)
# restore the nucleotide sequence and mutations to maintain expected behavior
self.tt.geogtr = self.tt.gtr
self.tt.geogtr.alphabet_to_location = alphabet
self.tt._gtr = sequence_gtr
self.dump_attr.append(attr)
if hasattr(self.tt.tree, 'sequence_LH'):
self.tt.tree.geo_LH = self.tt.tree.sequence_LH
self.tt.tree.sequence_LH = nuc_seq_LH
for node in self.tree.find_clades():
node.attr[attr] = alphabet[node.sequence[0]]
if node in nuc_seqs:
node.sequence = nuc_seqs[node]
if node.up is not None:
node.__setattr__(attr + '_transitions', node.mutations)
if node in nuc_muts:
node.mutations = nuc_muts[node]
self.tt.use_mutation_length = tmp_use_mutation_length