def get_response_content(fs):
# get the tree
tree = Newick.parse(fs.tree, Newick.NewickTree)
tree.assert_valid()
# get the sequence order if it exists
ordered_names = Util.get_stripped_lines(fs.order.splitlines())
if ordered_names:
observed_name_set = set(ordered_names)
expected_name_set = set(node.get_name() for node in tree.gen_tips())
extra_names = observed_name_set - expected_name_set
missing_names = expected_name_set - observed_name_set
if extra_names:
msg_a = 'the list of ordered names includes these names '
msg_b = 'not found in the tree: %s' % str(tuple(extra_names))
raise HandlingError(msg_a + msg_b)
if missing_names:
msg_a = 'the tree includes these names not found in the list '
msg_b = 'of ordered names: %s' % str(tuple(missing_names))
raise HandlingError(msg_a + msg_b)
else:
ordered_names = list(tip.get_name() for name in tree.gen_tips())
# do the sampling
sampled_sequences = JC69.sample_sequences(tree, ordered_names, fs.length)
alignment = Fasta.create_alignment(ordered_names, sampled_sequences)
# return the response
return alignment.to_fasta_string() + '\n'
def get_response_content(fs):
# get the tree
tree = Newick.parse(fs.tree, Newick.NewickTree)
tree.assert_valid()
# get the sequence order if it exists
ordered_names = Util.get_stripped_lines(fs.order.splitlines())
if ordered_names:
observed_name_set = set(ordered_names)
expected_name_set = set(node.get_name() for node in tree.gen_tips())
extra_names = observed_name_set - expected_name_set
missing_names = expected_name_set - observed_name_set
if extra_names:
msg_a = 'the list of ordered names includes these names '
msg_b = 'not found in the tree: %s' % str(tuple(extra_names))
raise HandlingError(msg_a + msg_b)
if missing_names:
msg_a = 'the tree includes these names not found in the list '
msg_b = 'of ordered names: %s' % str(tuple(missing_names))
raise HandlingError(msg_a + msg_b)
else:
ordered_names = list(tip.get_name() for name in tree.gen_tips())
# do the sampling
sampled_sequences = JC69.sample_sequences(tree, ordered_names, fs.length)
alignment = Fasta.create_alignment(ordered_names, sampled_sequences)
# return the response
return alignment.to_fasta_string() + '\n'
def get_amino_acid_alignment(table):
"""
@param table: a table of data in some random format sent by Ferran Casals
@return: a Fasta amino acid alignment object
"""
if len(table) < 2:
raise HandlingError('the data table should have at least two rows')
first_row = table[0]
if len(first_row) < 6:
raise HandlingError(
'the first row of the table has %d columns '
'but at least six were expected' % len(first_row))
if first_row[0].upper() != 'variant'.upper():
raise HandlingError('expected the first column to be the variant')
if first_row[1].upper() != 'chr'.upper():
raise HandlingError('expected the second column to be the chromosome')
if first_row[2].upper() != 'position'.upper():
raise HandlingError('expected the third column to be the position')
if first_row[3].upper() != 'Amino Acid Change'.upper():
raise HandlingError(
'expected the fourth column to be the amino acid change')
if first_row[4].upper() != 'alleles'.upper():
raise HandlingError(
'expected the fifth column to be the nucleotide change')
remaining_rows = table[1:]
for row in remaining_rows:
if len(row) != len(first_row):
raise HandlingError(
'each row should have the same number of columns')
# get the ordered taxa
taxa = first_row[5:]
if len(set(taxa)) != len(taxa):
raise HandlingError('the same taxon appears in more than one column')
# get the sequence of codons for each taxon
codon_sequences = zip(*remaining_rows)[5:]
# convert codon sequences to amino acid sequences
aa_sequences = []
for codon_sequence in codon_sequences:
aa_list = []
for codon in codon_sequence:
codon = codon.upper()
if codon == 'ND':
aa = '-'
elif codon in Codon.g_non_stop_codons:
aa = Codon.g_codon_to_aa_letter[codon]
elif codon in Codon.g_stop_codons:
raise HandlingError(
'one of the codons is a stop codon: %s' % codon)
else:
raise HandlingError(
'one of the codons is invalid: %s' % codon)
aa_list.append(aa)
aa_sequences.append(''.join(aa_list))
# return the alignment
return Fasta.create_alignment(taxa, aa_sequences)
def get_amino_acid_alignment(table):
"""
@param table: a table of data in some random format sent by Ferran Casals
@return: a Fasta amino acid alignment object
"""
if len(table) < 2:
raise HandlingError('the data table should have at least two rows')
first_row = table[0]
if len(first_row) < 6:
raise HandlingError('the first row of the table has %d columns '
'but at least six were expected' % len(first_row))
if first_row[0].upper() != 'variant'.upper():
raise HandlingError('expected the first column to be the variant')
if first_row[1].upper() != 'chr'.upper():
raise HandlingError('expected the second column to be the chromosome')
if first_row[2].upper() != 'position'.upper():
raise HandlingError('expected the third column to be the position')
if first_row[3].upper() != 'Amino Acid Change'.upper():
raise HandlingError(
'expected the fourth column to be the amino acid change')
if first_row[4].upper() != 'alleles'.upper():
raise HandlingError(
'expected the fifth column to be the nucleotide change')
remaining_rows = table[1:]
for row in remaining_rows:
if len(row) != len(first_row):
raise HandlingError(
'each row should have the same number of columns')
# get the ordered taxa
taxa = first_row[5:]
if len(set(taxa)) != len(taxa):
raise HandlingError('the same taxon appears in more than one column')
# get the sequence of codons for each taxon
codon_sequences = zip(*remaining_rows)[5:]
# convert codon sequences to amino acid sequences
aa_sequences = []
for codon_sequence in codon_sequences:
aa_list = []
for codon in codon_sequence:
codon = codon.upper()
if codon == 'ND':
aa = '-'
elif codon in Codon.g_non_stop_codons:
aa = Codon.g_codon_to_aa_letter[codon]
elif codon in Codon.g_stop_codons:
raise HandlingError('one of the codons is a stop codon: %s' %
codon)
else:
raise HandlingError('one of the codons is invalid: %s' % codon)
aa_list.append(aa)
aa_sequences.append(''.join(aa_list))
# return the alignment
return Fasta.create_alignment(taxa, aa_sequences)
def get_response_content(fs):
# init the response and get the user variables
out = StringIO()
nleaves = fs.nleaves
nvertices = nleaves * 2 - 1
nbranches = nvertices - 1
nsites = fs.nsites
# sample the coalescent tree with timelike branch lengths
R, B = kingman.sample(fs.nleaves)
r = Ftree.R_to_root(R)
# get the leaf vertex names
N = dict(zip(range(nleaves), string.uppercase[:nleaves]))
N_leaves = dict(N)
# get the internal vertex names
v_to_leaves = R_to_v_to_leaves(R)
for v, leaves in sorted(v_to_leaves.items()):
if len(leaves) > 1:
N[v] = ''.join(sorted(N[leaf] for leaf in leaves))
# get vertex ages
v_to_age = kingman.RB_to_v_to_age(R, B)
# sample the rates on the branches
b_to_rate = sample_b_to_rate(R)
xycorr = get_correlation(R, b_to_rate)
# define B_subs in terms of substitutions instead of time
B_subs = dict((p, t * b_to_rate[p]) for p, t in B.items())
# sample the alignment
v_to_seq = sample_v_to_seq(R, B_subs, nsites)
# get the log likelihood; this is kind of horrible
pairs = [(N[v], ''.join(v_to_seq[v])) for v in range(nleaves)]
headers, sequences = zip(*pairs)
alignment = Fasta.create_alignment(headers, sequences)
newick_string = FtreeIO.RBN_to_newick(R, B_subs, N_leaves)
tree = Newick.parse(newick_string, Newick.NewickTree)
dictionary_rate_matrix = RateMatrix.get_jukes_cantor_rate_matrix()
ordered_states = list('ACGT')
row_major_rate_matrix = MatrixUtil.dict_to_row_major(
dictionary_rate_matrix, ordered_states, ordered_states)
rate_matrix_object = RateMatrix.RateMatrix(
row_major_rate_matrix, ordered_states)
ll = PhyLikelihood.get_log_likelihood(
tree, alignment, rate_matrix_object)
# get ll when rates are all 1.0
newick_string = FtreeIO.RBN_to_newick(R, B, N_leaves)
tree = Newick.parse(newick_string, Newick.NewickTree)
ll_unity = PhyLikelihood.get_log_likelihood(
tree, alignment, rate_matrix_object)
# get ll when rates are numerically optimized
# TODO incorporate the result into the xml file
# TODO speed up the likelihood evaluation (beagle? C module?)
#f = Opt(R, B, N_leaves, alignment)
#X_logs = [0.0] * nbranches
#result = scipy.optimize.fmin(f, X_logs, full_output=True)
#print result
#
print >> out, '<?xml version="1.0"?>'
print >> out, '<beast>'
print >> out
print >> out, '<!-- actual rate autocorrelation', xycorr, '-->'
print >> out, '<!-- actual root height', v_to_age[r], '-->'
print >> out, '<!-- actual log likelihood', ll, '-->'
print >> out, '<!-- ll if rates were unity', ll_unity, '-->'
print >> out
print >> out, '<!--'
print >> out, 'predefine the taxa as in'
print >> out, 'http://beast.bio.ed.ac.uk/Introduction_to_XML_format'
print >> out, '-->'
print >> out, get_leaf_taxon_defn(list(string.uppercase[:nleaves]))
print >> out
print >> out, '<!--'
print >> out, 'define the alignment as in'
print >> out, 'http://beast.bio.ed.ac.uk/Introduction_to_XML_format'
print >> out, '-->'
print >> out, get_alignment_defn(leaves, N, v_to_seq)
print >> out
print >> out, '<!--'
print >> out, 'specify the starting tree as in'
print >> out, 'http://beast.bio.ed.ac.uk/Tutorial_4'
print >> out, '-->'
print >> out, get_starting_tree_defn(R, B, N_leaves)
print >> out
print >> out, '<!--'
print >> out, 'connect the tree model as in'
print >> out, 'http://beast.bio.ed.ac.uk/Tutorial_4'
print >> out, '-->'
print >> out, g_tree_model_defn
print >> out
print >> out, g_uncorrelated_relaxed_clock_info
print >> out
"""
print >> out, '<!--'
print >> out, 'create a list of taxa for which to constrain the mrca as in'
print >> out, 'http://beast.bio.ed.ac.uk/Tutorial_3.1'
print >> out, '-->'
for v, leaves in sorted(v_to_leaves.items()):
if len(leaves) > 1:
print >> out, get_mrca_subset_defn(N, v, leaves)
print >> out
print >> out, '<!--'
print >> out, 'create a tmrcaStatistic that will record the height as in'
print >> out, 'http://beast.bio.ed.ac.uk/Tutorial_3.1'
print >> out, '-->'
for v, leaves in sorted(v_to_leaves.items()):
if len(leaves) > 1:
print >> out, get_mrca_stat_defn(N[v])
"""
print >> out
print >> out, g_likelihood_info
print >> out
print >> out, '<!--'
print >> out, 'run the mcmc'
print >> out, 'http://beast.bio.ed.ac.uk/Tutorial_3.1'
print >> out, '-->'
print >> out, get_mcmc_defn(v_to_leaves, v_to_age, N)
print >> out
print >> out, '</beast>'
# return the response
return out.getvalue()
