def test_dn_ds(self):
from Bio.CodonAlign.CodonSeq import cal_dn_ds
codon_seq1 = self.aln[0]
codon_seq2 = self.aln[1]
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='NG86')
self.assertAlmostEqual(round(dN, 4), 0.0209, places=4)
self.assertAlmostEqual(round(dS, 4), 0.0178, places=4)
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='LWL85')
self.assertAlmostEqual(round(dN, 4), 0.0203, places=4)
self.assertAlmostEqual(round(dS, 4), 0.0164, places=4)
try:
import scipy
except ImportError:
# Silently skip the rest of the test
return
# This should be present:
from scipy.linalg import expm
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='YN00')
self.assertAlmostEqual(round(dN, 4), 0.0198, places=4)
self.assertAlmostEqual(round(dS, 4), 0.0222, places=4)
try:
# New in scipy v0.11
from scipy.optimize import minimize
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='ML')
self.assertAlmostEqual(round(dN, 4), 0.0194, places=4)
self.assertAlmostEqual(round(dS, 4), 0.0217, places=4)
except ImportError:
# TODO - Show a warning?
pass
def test_dn_ds(self):
from Bio.CodonAlign.CodonSeq import cal_dn_ds
codon_seq1 = self.aln[0]
codon_seq2 = self.aln[1]
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='NG86')
self.assertAlmostEquals(round(dN, 4), 0.0209, places=4)
self.assertAlmostEquals(round(dS, 4), 0.0178, places=4)
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='LWL85')
self.assertAlmostEquals(round(dN, 4), 0.0203, places=4)
self.assertAlmostEquals(round(dS, 4), 0.0164, places=4)
try:
from scipy.linalg import expm
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='YN00')
self.assertAlmostEquals(round(dN, 4), 0.0198, places=4)
self.assertAlmostEquals(round(dS, 4), 0.0222, places=4)
except ImportError:
warnings.warn(
'Importing scipy.linalg.expm failed. Skip testing ML method for dN/dS estimation'
)
pass
try:
from scipy.optimize import minimize
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='ML')
self.assertAlmostEquals(round(dN, 4), 0.0194, places=4)
self.assertAlmostEquals(round(dS, 4), 0.0217, places=4)
except ImportError:
warnings.warn(
'Importing scipy.optimize.minimize failed. Skip testing ML method for dN/dS estimation'
)
pass
def test_dn_ds(self):
from Bio.CodonAlign.CodonSeq import cal_dn_ds
codon_seq1 = self.aln[0]
codon_seq2 = self.aln[1]
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='NG86')
self.assertAlmostEqual(round(dN, 4), 0.0209, places=4)
self.assertAlmostEqual(round(dS, 4), 0.0178, places=4)
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='LWL85')
self.assertAlmostEqual(round(dN, 4), 0.0203, places=4)
self.assertAlmostEqual(round(dS, 4), 0.0164, places=4)
try:
import scipy
except ImportError:
# Silently skip the rest of the test
return
# This should be present:
from scipy.linalg import expm
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='YN00')
self.assertAlmostEqual(round(dN, 4), 0.0198, places=4)
self.assertAlmostEqual(round(dS, 4), 0.0222, places=4)
try:
# New in scipy v0.11
from scipy.optimize import minimize
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='ML')
self.assertAlmostEqual(round(dN, 4), 0.0194, places=4)
self.assertAlmostEqual(round(dS, 4), 0.0217, places=4)
except ImportError:
# TODO - Show a warning?
pass
def test_dn_ds(self):
from Bio.CodonAlign.CodonSeq import cal_dn_ds
codon_seq1 = self.aln[0]
codon_seq2 = self.aln[1]
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='NG86')
self.assertAlmostEquals(round(dN, 4), 0.0209, places=4)
self.assertAlmostEquals(round(dS, 4), 0.0178, places=4)
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='LWL85')
self.assertAlmostEquals(round(dN, 4), 0.0203, places=4)
self.assertAlmostEquals(round(dS, 4), 0.0164, places=4)
try:
from scipy.linalg import expm
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='YN00')
self.assertAlmostEquals(round(dN, 4), 0.0198, places=4)
self.assertAlmostEquals(round(dS, 4), 0.0222, places=4)
except ImportError:
warnings.warn('Importing scipy.linalg.expm failed. Skip testing ML method for dN/dS estimation')
pass
try:
from scipy.optimize import minimize
dN, dS = cal_dn_ds(codon_seq1, codon_seq2, method='ML')
self.assertAlmostEquals(round(dN, 4), 0.0194, places=4)
self.assertAlmostEquals(round(dS, 4), 0.0217, places=4)
except ImportError:
warnings.warn('Importing scipy.optimize.minimize failed. Skip testing ML method for dN/dS estimation')
pass
def get_dn_ds_matrix(self, method="NG86"):
"""Available methods include NG86, LWL85, YN00 and ML.
"""
from Bio.Phylo.TreeConstruction import _DistanceMatrix as DM
names = [i.id for i in self._records]
size = len(self._records)
dn_matrix = []
ds_matrix = []
for i in range(size):
dn_matrix.append([])
ds_matrix.append([])
for j in range(i+1):
if i != j:
dn, ds = cal_dn_ds(self._records[i], self._records[j],
method=method)
dn_matrix[i].append(dn)
ds_matrix[i].append(ds)
else:
dn_matrix[i].append(0.0)
ds_matrix[i].append(0.0)
dn_dm = DM(names, matrix=dn_matrix)
ds_dm = DM(names, matrix=ds_matrix)
return dn_dm, ds_dm
def get_dn_ds_matrix(self, method="NG86"):
"""Available methods include NG86, LWL85, YN00 and ML.
"""
from Bio.Phylo.TreeConstruction import _DistanceMatrix as DM
names = [i.id for i in self._records]
size = len(self._records)
dn_matrix = []
ds_matrix = []
for i in range(size):
dn_matrix.append([])
ds_matrix.append([])
for j in range(i + 1):
if i != j:
dn, ds = cal_dn_ds(self._records[i],
self._records[j],
method=method)
dn_matrix[i].append(dn)
ds_matrix[i].append(ds)
else:
dn_matrix[i].append(0.0)
ds_matrix[i].append(0.0)
dn_dm = DM(names, matrix=dn_matrix)
ds_dm = DM(names, matrix=ds_matrix)
return dn_dm, ds_dm
