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# Copyright 2016 by Peter Cock. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""Bio.Align.AlignInfo related tests."""
import unittest
from Bio.Alphabet import DNAAlphabet, generic_protein
from Bio.Alphabet import HasStopCodon, Gapped
from Bio.Alphabet.IUPAC import unambiguous_dna
from Bio.Align import MultipleSeqAlignment
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio import AlignIO
from Bio.SubsMat.FreqTable import FreqTable, FREQ
from Bio.Align.AlignInfo import SummaryInfo
import math
class AlignInfoTests(unittest.TestCase):
"""Test basic usage."""
def assertAlmostEqualList(self, list1, list2, **kwargs):
self.assertEqual(len(list1), len(list2))
for (v1, v2) in zip(list1, list2):
self.assertAlmostEqual(v1, v2, **kwargs)
def test_nucleotides(self):
filename = "GFF/multi.fna"
format = "fasta"
alignment = AlignIO.read(filename, format, alphabet=unambiguous_dna)
summary = SummaryInfo(alignment)
c = summary.dumb_consensus(ambiguous="N")
self.assertEqual(str(c), 'NNNNNNNN')
self.assertNotEqual(c.alphabet, unambiguous_dna)
self.assertTrue(isinstance(c.alphabet, DNAAlphabet))
c = summary.gap_consensus(ambiguous="N")
self.assertEqual(str(c), 'NNNNNNNN')
self.assertNotEqual(c.alphabet, unambiguous_dna)
self.assertTrue(isinstance(c.alphabet, DNAAlphabet))
expected = FreqTable({"A": 0.25, "G": 0.25, "T": 0.25, "C": 0.25},
FREQ, unambiguous_dna)
m = summary.pos_specific_score_matrix(chars_to_ignore=['-'],
axis_seq=c)
self.assertEqual(str(m), """ A C G T
N 2.0 0.0 1.0 0.0
N 1.0 1.0 1.0 0.0
N 1.0 0.0 2.0 0.0
N 0.0 1.0 1.0 1.0
N 1.0 2.0 0.0 0.0
N 0.0 2.0 1.0 0.0
N 1.0 2.0 0.0 0.0
N 0.0 2.0 1.0 0.0
""")
# Have a generic alphabet, without a declared gap char, so must tell
# provide the frequencies and chars to ignore explicitly.
ic = summary.information_content(e_freq_table=expected,
chars_to_ignore=['-'])
self.assertAlmostEqual(ic, 7.32029999423075, places=6)
def test_proteins(self):
alpha = HasStopCodon(Gapped(generic_protein, "-"), "*")
a = MultipleSeqAlignment([
SeqRecord(Seq("MHQAIFIYQIGYP*LKSGYIQSIRSPEYDNW-", alpha), id="ID001"),
SeqRecord(Seq("MH--IFIYQIGYAYLKSGYIQSIRSPEY-NW*", alpha), id="ID002"),
SeqRecord(Seq("MHQAIFIYQIGYPYLKSGYIQSIRSPEYDNW*", alpha), id="ID003")])
self.assertEqual(32, a.get_alignment_length())
s = SummaryInfo(a)
c = s.dumb_consensus(ambiguous="X")
self.assertEqual(str(c), "MHQAIFIYQIGYXXLKSGYIQSIRSPEYDNW*")
c = s.gap_consensus(ambiguous="X")
self.assertEqual(str(c), "MHXXIFIYQIGYXXLKSGYIQSIRSPEYXNWX")
m = s.pos_specific_score_matrix(chars_to_ignore=['-', '*'], axis_seq=c)
self.assertEqual(str(m), """ A D E F G H I K L M N P Q R S W Y
M 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
H 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
X 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0
X 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
I 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
F 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
I 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Y 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0
Q 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0
I 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
G 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Y 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0
X 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0
X 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0
L 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
K 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
S 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0
G 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Y 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0
I 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Q 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0
S 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0
I 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
R 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0
S 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0
P 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0
E 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Y 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0
X 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
N 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0
W 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0
X 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
""")
ic = s.information_content(chars_to_ignore=['-', '*'])
self.assertAlmostEqual(ic, 133.061475107, places=6)
def test_pseudo_count(self):
# use example from
# http://biologie.univ-mrs.fr/upload/p202/01.4.PSSM_theory.pdf
alpha = unambiguous_dna
dna_align = MultipleSeqAlignment([
SeqRecord(Seq("AACCACGTTTAA", alpha), id="ID001"),
SeqRecord(Seq("CACCACGTGGGT", alpha), id="ID002"),
SeqRecord(Seq("CACCACGTTCGC", alpha), id="ID003"),
SeqRecord(Seq("GCGCACGTGGGG", alpha), id="ID004"),
SeqRecord(Seq("TCGCACGTTGTG", alpha), id="ID005"),
SeqRecord(Seq("TGGCACGTGTTT", alpha), id="ID006"),
SeqRecord(Seq("TGACACGTGGGA", alpha), id="ID007"),
SeqRecord(Seq("TTACACGTGCGC", alpha), id="ID008")])
summary = SummaryInfo(dna_align)
expected = FreqTable({"A": 0.325, "G": 0.175, "T": 0.325, "C": 0.175},
FREQ, unambiguous_dna)
ic = summary.information_content(e_freq_table=expected,
log_base=math.exp(1),
pseudo_count=1)
self.assertAlmostEqualList(summary.ic_vector, [0.110, 0.090, 0.360, 1.290,
0.800, 1.290, 1.290, 0.80,
0.610, 0.390, 0.470, 0.040],
places=2)
self.assertAlmostEqual(ic, 7.546, places=3)
if __name__ == "__main__":
runner = unittest.TextTestRunner(verbosity=2)
unittest.main(testRunner=runner)
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