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$ ruff check --fix --select=I \ --config=lint.isort.force-single-line=true \ --config=lint.isort.order-by-type=false \ BioSQL/ Bio/ Tests/ Scripts/ Doc/ setup.py Using ruff version 0.4.10
226 lines
9.1 KiB
Python
226 lines
9.1 KiB
Python
# Copyright 2016 by Peter Cock. All rights reserved.
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# This code is part of the Biopython distribution and governed by its
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# license. Please see the LICENSE file that should have been included
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# as part of this package.
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"""Bio.Align.AlignInfo related tests."""
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import math
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import unittest
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from Bio import AlignIO
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from Bio import BiopythonDeprecationWarning
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from Bio.Align import MultipleSeqAlignment
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from Bio.Align.AlignInfo import SummaryInfo
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from Bio.Data import IUPACData
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from Bio.motifs import Motif
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from Bio.Seq import Seq
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from Bio.SeqRecord import SeqRecord
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class AlignInfoTests(unittest.TestCase):
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"""Test basic usage."""
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def assertAlmostEqualList(self, list1, list2, **kwargs):
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self.assertEqual(len(list1), len(list2))
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for v1, v2 in zip(list1, list2):
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self.assertAlmostEqual(v1, v2, **kwargs)
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def test_nucleotides(self):
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filename = "GFF/multi.fna"
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fmt = "fasta"
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msa = AlignIO.read(filename, fmt)
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summary = SummaryInfo(msa)
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alignment = msa.alignment
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motif = Motif("ACGT", alignment)
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with self.assertWarns(BiopythonDeprecationWarning):
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c = summary.dumb_consensus(threshold=0.1, ambiguous="N")
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# dumb_consensus uses ambiguous if multiple letters have the same score
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self.assertEqual(c, "ANGNCCCC")
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c = motif.counts.calculate_consensus(identity=0.1)
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# Instead, EMBOSS uses the first letter it encounters
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self.assertEqual(c, "AaGcCCCC")
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with self.assertWarns(BiopythonDeprecationWarning):
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c = summary.dumb_consensus(ambiguous="N")
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self.assertEqual(c, "NNNNNNNN")
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c = motif.counts.calculate_consensus(identity=0.7)
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self.assertEqual(c, "NNNNNNNN")
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with self.assertWarns(BiopythonDeprecationWarning):
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c = summary.gap_consensus(ambiguous="N")
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self.assertEqual(c, "NNNNNNNN")
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expected = {"A": 0.25, "G": 0.25, "T": 0.25, "C": 0.25}
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with self.assertWarns(BiopythonDeprecationWarning):
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m = summary.pos_specific_score_matrix(chars_to_ignore=["-"], axis_seq=c)
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counts = motif.counts
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for i in range(alignment.length):
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for letter in "ACGT":
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self.assertAlmostEqual(counts[letter][i], m[i][letter])
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self.assertEqual(
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str(m),
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""" A C G T
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N 2.0 0.0 1.0 0.0
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N 1.0 1.0 1.0 0.0
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N 1.0 0.0 2.0 0.0
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N 0.0 1.0 1.0 1.0
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N 1.0 2.0 0.0 0.0
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N 0.0 2.0 1.0 0.0
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N 1.0 2.0 0.0 0.0
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N 0.0 2.0 1.0 0.0
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""",
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)
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# provide the frequencies and chars to ignore explicitly.
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with self.assertWarns(BiopythonDeprecationWarning):
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ic = summary.information_content(
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e_freq_table=expected, chars_to_ignore=["-"]
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)
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self.assertAlmostEqual(ic, 7.32029999423075)
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ic = sum(motif.relative_entropy)
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self.assertAlmostEqual(ic, 7.32029999423075)
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def test_proteins(self):
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letters = IUPACData.protein_letters
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a = MultipleSeqAlignment(
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[
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SeqRecord(Seq("MHQAIFIYQIGYP*LKSGYIQSIRSPEYDNW-"), id="ID001"),
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SeqRecord(Seq("MH--IFIYQIGYAYLKSGYIQSIRSPEY-NW*"), id="ID002"),
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SeqRecord(Seq("MHQAIFIYQIGYPYLKSGYIQSIRSPEYDNW*"), id="ID003"),
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]
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)
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self.assertEqual(32, a.get_alignment_length())
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s = SummaryInfo(a)
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alignment = a.alignment
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motif = Motif(letters + "*", alignment)
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counts = motif.counts
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with self.assertWarns(BiopythonDeprecationWarning):
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dumb_consensus = s.dumb_consensus()
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self.assertEqual(dumb_consensus, "MHQAIFIYQIGYXXLKSGYIQSIRSPEYDNW*")
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consensus = counts.calculate_consensus(identity=0.7)
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self.assertEqual(consensus, dumb_consensus)
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with self.assertWarns(BiopythonDeprecationWarning):
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c = s.gap_consensus(ambiguous="X")
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self.assertEqual(c, "MHXXIFIYQIGYXXLKSGYIQSIRSPEYXNWX")
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with self.assertWarns(BiopythonDeprecationWarning):
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m = s.pos_specific_score_matrix(chars_to_ignore=["-", "*"], axis_seq=c)
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j = 0
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all_letters = s._get_all_letters()
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for i in range(alignment.length):
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for letter in letters:
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count = counts[letter][i]
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if letter in all_letters:
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self.assertAlmostEqual(count, m[j][letter])
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else:
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self.assertAlmostEqual(count, 0.0)
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j += 1
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self.assertEqual(
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str(m),
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""" A D E F G H I K L M N P Q R S W Y
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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""",
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)
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base_freq = 1.0 / len(letters)
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e_freq_table = {letter: base_freq for letter in letters}
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with self.assertWarns(BiopythonDeprecationWarning):
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ic = s.information_content(
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e_freq_table=e_freq_table, chars_to_ignore=["-", "*"]
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)
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self.assertAlmostEqual(ic, 133.061475107)
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motif = Motif(letters, alignment)
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ic = sum(motif.relative_entropy)
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self.assertAlmostEqual(ic, 133.061475107)
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def test_pseudo_count(self):
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# use example from
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# http://biologie.univ-mrs.fr/upload/p202/01.4.PSSM_theory.pdf
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msa = MultipleSeqAlignment(
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[
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SeqRecord(Seq("AACCACGTTTAA"), id="ID001"),
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SeqRecord(Seq("CACCACGTGGGT"), id="ID002"),
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SeqRecord(Seq("CACCACGTTCGC"), id="ID003"),
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SeqRecord(Seq("GCGCACGTGGGG"), id="ID004"),
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SeqRecord(Seq("TCGCACGTTGTG"), id="ID005"),
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SeqRecord(Seq("TGGCACGTGTTT"), id="ID006"),
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SeqRecord(Seq("TGACACGTGGGA"), id="ID007"),
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SeqRecord(Seq("TTACACGTGCGC"), id="ID008"),
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]
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)
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summary = SummaryInfo(msa)
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expected = {"A": 0.325, "G": 0.175, "T": 0.325, "C": 0.175}
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with self.assertWarns(BiopythonDeprecationWarning):
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ic = summary.information_content(
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e_freq_table=expected, log_base=math.exp(1), pseudo_count=1
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)
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self.assertAlmostEqual(ic, 7.546369561463767)
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ic_vector = [
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0.11112361,
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0.08677812,
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0.35598044,
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1.29445419,
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0.80272907,
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1.29445419,
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1.29445419,
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0.80272907,
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0.60929642,
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0.39157892,
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0.46539767,
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0.03739368,
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]
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self.assertAlmostEqualList(summary.ic_vector, ic_vector)
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# One more time, now using a new-style Alignment object:
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alignment = msa.alignment
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motif = Motif("ACGT", alignment)
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motif.background = expected
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motif.pseudocounts = expected
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self.assertAlmostEqualList(motif.relative_entropy * math.log(2), ic_vector)
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ic = sum(ic_vector)
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self.assertAlmostEqual(ic, 7.546369561463767)
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if __name__ == "__main__":
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runner = unittest.TextTestRunner(verbosity=2)
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unittest.main(testRunner=runner)
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