Python MutableSeq.toseq方法代码示例

# 需要导入模块: from Bio.Seq import MutableSeq [as 别名]
# 或者: from Bio.Seq.MutableSeq import toseq [as 别名]
def generate_rolls(num_rolls):
    """Generate a bunch of rolls corresponding to the casino probabilities.

    Returns:

    - The generate roll sequence
    - The state sequence that generated the roll.

    """
    # start off in the fair state
    cur_state = 'F'
    roll_seq = MutableSeq('', DiceRollAlphabet())
    state_seq = MutableSeq('', DiceTypeAlphabet())

    # generate the sequence
    for roll in range(num_rolls):
        state_seq.append(cur_state)
        # generate a random number
        chance_num = random.random()

        # add on a new roll to the sequence
        new_roll = _loaded_dice_roll(chance_num, cur_state)
        roll_seq.append(new_roll)

        # now give us a chance to switch to a new state
        chance_num = random.random()
        if cur_state == 'F':
            if chance_num <= .05:
                cur_state = 'L'
        elif cur_state == 'L':
            if chance_num <= .1:
                cur_state = 'F'

    return roll_seq.toseq(), state_seq.toseq()

# 需要导入模块: from Bio.Seq import MutableSeq [as 别名]
# 或者: from Bio.Seq.MutableSeq import toseq [as 别名]
 def get_optimal_alignment(self):
     """Follow the traceback to get the optimal alignment."""
     # intialize the two sequences which will return the alignment
     align_seq1 = MutableSeq(array.array("c"), 
       Alphabet.Gapped(IUPAC.protein, GAP_CHAR))
     align_seq2 = MutableSeq(array.array("c"), 
       Alphabet.Gapped(IUPAC.protein, GAP_CHAR))
       
     # take care of the initial case with the bottom corner matrix
     # item
     current_cell = self.dpmatrix[(len(self.seq1), len(self.seq2))]
     align_seq1.append(current_cell.seq1item)
     align_seq2.append(current_cell.seq2item)
     
     next_cell = current_cell.get_parent()
     current_cell = next_cell
     next_cell = current_cell.get_parent()
     
     # keeping adding sequence until we reach (0, 0)
     while next_cell:
         # add the new sequence--three cases:
         # 1. Move up diaganolly, add a new seq1 and seq2 to the 
         # aligned sequences
         if ((next_cell.col_pos == current_cell.col_pos - 1) and
           (next_cell.row_pos == current_cell.row_pos - 1)):
             # print "case 1 -> seq1 %s, seq2 %s" % (
             # current_cell.seq1item, current_cell.seq2item)
             align_seq1.append(current_cell.seq1item)
             align_seq2.append(current_cell.seq2item)
         # 2. Move upwards, add a new seq2 and a gap in seq1
         elif ((next_cell.col_pos  == current_cell.col_pos) and
           (next_cell.row_pos == current_cell.row_pos - 1)):
             #print "case 2 -> seq2 %s" % current_cell.seq2item
             align_seq1.append(GAP_CHAR)
             align_seq2.append(current_cell.seq2item)
         # 3. Move to the right, add a new seq1 and a gap in seq2
         elif ((next_cell.col_pos == current_cell.col_pos - 1) and
           (next_cell.row_pos == current_cell.row_pos)):
             #print "case 3 -> seq1 % s" % current_cell.seq1item
             align_seq1.append(current_cell.seq1item)
             align_seq2.append(GAP_CHAR)
         
         # now move on to the next sequence
         current_cell = next_cell
         next_cell = current_cell.get_parent()
     
     # reverse the returned alignments since we are reading them in
     # backwards
     align_seq1.reverse()
     align_seq2.reverse()
     return align_seq1.toseq(), align_seq2.toseq()

# 需要导入模块: from Bio.Seq import MutableSeq [as 别名]
# 或者: from Bio.Seq.MutableSeq import toseq [as 别名]
def Gthg01471():
    ori=Seq("ATGAGCATAAGTTTATCGGTTCCAAAATGGTTATTAACAGTTTTATCAATTTTATCTTTAGTCGTAGCATTTATTTTCGGTACCGTTTCCAATGCATCAGCAACAATTAACTATGGGGAGGAAGTCGCGGCAGTAGCAAATGACTATGTAGGAAGCCCATATAAATATGGAGGTACAACGCCAAAAGGATTTGATGCGAGTGGCTTTACTCAGTATGTGTATAAAAATGCTGCAACCAAATTGGCTATTCCGCGAACGAGTGCCGCACAGTATAAAGTCGGTAAATTTGTTAAACAAAGTGCGTTACAAAGAGGCGATTTAGTGTTTTATGCAACAGGAGCAAAAGGAAAGGTATCCTTTGTGGGAATTTATAATGGAAATGGTACGTTTATTGGTGCCACATCAAAAGGGGTAAAAGTGGTTAAAATGAGTGATAAATATTGGAAAGACCGGTATATAGGGGCTAAGCGAGTCATTAAGTAA", IUPAC.unambiguous_dna)
    mut=MutableSeq("ATGAGCATAAGTTTATCGGTTCCAAAATGGTTATTAACAGTTTTATCAATTTTATCTTTAGTCGTAGCATTTATTTTCGGTACCGTTTCCAATGCATCAGCAACAATTAACTATGGGGAGGAAGTCGCGGCAGTAGCAAATGACTATGTAGGAAGCCCATATAAATATGGAGGTACAACGCCAAAAGGATTTGATGCGAGTGGCTTTACTCAGTATGTGTATAAAAATGCTGCAACCAAATTGGCTATTCCGCGAACGAGTGCCGCACAGTATAAAGTCGGTAAATTTGTTAAACAAAGTGCGTTACAAAGAGGCGATTTAGTGTTTTATGCAACAGGAGCAAAAGGAAAGGTATCCTTTGTGGGAATTTATAATGGAAATGGTACGTTTATTGGTGCCACATCAAAAGGGGTAAAAGTGGTTAAAATGAGTGATAAATATTGGAAAGACCGGTATATAGGGGCTAAGCGAGTCATTAAGTAA", IUPAC.unambiguous_dna)

    a="AGTCGA"
    b="GACTAG"
    for i,v in enumerate([259,277,282,295,299,306]):
        print(mut[v-1]+a[i])
        mut[v-1]=b[i]
    print(ori.translate())
    print(mut.toseq().translate())

# 需要导入模块: from Bio.Seq import MutableSeq [as 别名]
# 或者: from Bio.Seq.MutableSeq import toseq [as 别名]

#.........这里部分代码省略.........
        of emissions.

        Arguments:

        o sequence -- A Seq object with the emission sequence that we
        want to decode.

        o state_alphabet -- The alphabet of the possible state sequences
        that can be generated.
        """

        # calculate logarithms of the initial, transition, and emission probs
        log_initial = self._log_transform(self.initial_prob)
        log_trans = self._log_transform(self.transition_prob)
        log_emission = self._log_transform(self.emission_prob)

        viterbi_probs = {}
        pred_state_seq = {}
        state_letters = state_alphabet.letters

        # --- recursion
        # loop over the training squence (i = 1 .. L)
        # NOTE: My index numbers are one less than what is given in Durbin
        # et al, since we are indexing the sequence going from 0 to
        # (Length - 1) not 1 to Length, like in Durbin et al.
        for i in range(0, len(sequence)):
            # loop over all of the possible i-th states in the state path
            for cur_state in state_letters:
                # e_{l}(x_{i})
                emission_part = log_emission[(cur_state, sequence[i])]

                max_prob = 0
                if i == 0:
                    # for the first state, use the initial probability rather
                    # than looking back to previous states
                    max_prob = log_initial[cur_state]
                else:
                    # loop over all possible (i-1)-th previous states
                    possible_state_probs = {}
                    for prev_state in self.transitions_to(cur_state):
                        # a_{kl}
                        trans_part = log_trans[(prev_state, cur_state)]

                        # v_{k}(i - 1)
                        viterbi_part = viterbi_probs[(prev_state, i - 1)]
                        cur_prob = viterbi_part + trans_part

                        possible_state_probs[prev_state] = cur_prob

                    # calculate the viterbi probability using the max
                    max_prob = max(possible_state_probs.values())

                # v_{k}(i)
                viterbi_probs[(cur_state, i)] = (emission_part + max_prob)

                if i > 0:
                    # get the most likely prev_state leading to cur_state
                    for state in possible_state_probs:
                        if possible_state_probs[state] == max_prob:
                            pred_state_seq[(i - 1, cur_state)] = state
                            break
                    
        # --- termination
        # calculate the probability of the state path
        # loop over all states
        all_probs = {}
        for state in state_letters:
            # v_{k}(L)
            all_probs[state] = viterbi_probs[(state, len(sequence) - 1)]

        state_path_prob = max(all_probs.values())

        # find the last pointer we need to trace back from
        last_state = ''
        for state in all_probs:
            if all_probs[state] == state_path_prob:
                last_state = state

        assert last_state != '', "Didn't find the last state to trace from!"
                
        # --- traceback
        traceback_seq = MutableSeq('', state_alphabet)
        
        loop_seq = range(1, len(sequence))
        loop_seq.reverse()

        # last_state is the last state in the most probable state sequence.
        # Compute that sequence by walking backwards in time. From the i-th
        # state in the sequence, find the (i-1)-th state as the most
        # probable state preceding the i-th state.
        state = last_state
        traceback_seq.append(state)
        for i in loop_seq:
            state = pred_state_seq[(i - 1, state)]
            traceback_seq.append(state)

        # put the traceback sequence in the proper orientation
        traceback_seq.reverse()

        return traceback_seq.toseq(), state_path_prob

# 需要导入模块: from Bio.Seq import MutableSeq [as 别名]
# 或者: from Bio.Seq.MutableSeq import toseq [as 别名]
    "AATCGTGGCTATTACTGGGATGGAGGTCACTGGCGCGACCACGGCTGGTGGAAACAACAT" +
    "TATGAATGGCGAGGCAATCGCTGGCACCTACACGGACCGCCGCCACCGCCGCGCCACCAT" +
    "AAGAAAGCTCCTCATGATCATCACGGCGGTCATGGTCCAGGCAAACATCACCGCTAA",
    generic_dna)

print(gene.translate(table="Bacterial"))
print(gene.translate(table="Bacterial", cds=True))

##查看密码子表
from Bio.Data import CodonTable
standard_table = CodonTable.unambiguous_dna_by_name["Standard"]
mito_table = CodonTable.unambiguous_dna_by_id[2]

print(standard_table)
print(mito_table.start_codons)
print(mito_table.stop_codons)
print(mito_table.forward_table["ACG"])

##可变对象
from Bio.Seq import MutableSeq
mutable_seq = MutableSeq("GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA", IUPAC.unambiguous_dna)
print(mutable_seq)
mutable_seq[5] = "C"
print(mutable_seq)
mutable_seq.remove("T")
print(mutable_seq)
mutable_seq.reverse()
print(mutable_seq)
new_seq = mutable_seq.toseq()
print(new_seq)

# 需要导入模块: from Bio.Seq import MutableSeq [as 别名]
# 或者: from Bio.Seq.MutableSeq import toseq [as 别名]

#.........这里部分代码省略.........
        This implements the Viterbi algorithm (see pgs 55-57 in Durbin et
        al for a full explanation -- this is where I took my implementation
        ideas from), to allow decoding of the state path, given a sequence
        of emissions.

        Arguments:

        o sequence -- A Seq object with the emission sequence that we
        want to decode.

        o state_alphabet -- The alphabet of the possible state sequences
        that can be generated.
        """
        # calculate logarithms of the transition and emission probs
        log_trans = self._log_transform(self.transition_prob)
        log_emission = self._log_transform(self.emission_prob)

        viterbi_probs = {}
        pred_state_seq = {}
        state_letters = state_alphabet.letters
        # --- initialization
        #
        # NOTE: My index numbers are one less than what is given in Durbin
        # et al, since we are indexing the sequence going from 0 to
        # (Length - 1) not 1 to Length, like in Durbin et al.
        #
        # v_{0}(0) = 1
        viterbi_probs[(state_letters[0], -1)] = 1
        # v_{k}(0) = 0 for k > 0
        for state_letter in state_letters[1:]:
            viterbi_probs[(state_letter, -1)] = 0

        # --- recursion
        # loop over the training squence (i = 1 .. L)
        for i in range(0, len(sequence)):
            # now loop over all of the letters in the state path
            for main_state in state_letters:
                # e_{l}(x_{i})
                emission_part = log_emission[(main_state, sequence[i])]

                # loop over all possible states
                possible_state_probs = {}
                for cur_state in self.transitions_from(main_state):
                    # a_{kl}
                    trans_part = log_trans[(cur_state, main_state)]

                    # v_{k}(i - 1)
                    viterbi_part = viterbi_probs[(cur_state, i - 1)]
                    cur_prob = viterbi_part + trans_part

                    possible_state_probs[cur_state] = cur_prob

                # finally calculate the viterbi probability using the max
                max_prob = max(possible_state_probs.values())
                viterbi_probs[(main_state, i)] = (emission_part + max_prob)

                # now get the most likely state
                for state in possible_state_probs:
                    if possible_state_probs[state] == max_prob:
                        pred_state_seq[(i - 1, main_state)] = state
                        break
                    
        # --- termination
        # calculate the probability of the state path
        # loop over all letters
        all_probs = {}
        for state in state_letters:
            # v_{k}(L)
            viterbi_part = viterbi_probs[(state, len(sequence) - 1)]
            # a_{k0}
            transition_part = log_trans[(state, state_letters[0])]

            all_probs[state] = viterbi_part * transition_part

        state_path_prob = max(all_probs.values())

        # find the last pointer we need to trace back from
        last_state = ''
        for state in all_probs:
            if all_probs[state] == state_path_prob:
                last_state = state

        assert last_state != '', "Didn't find the last state to trace from!"
                
        # --- traceback
        traceback_seq = MutableSeq('', state_alphabet)
        
        loop_seq = range(0, len(sequence))
        loop_seq.reverse()

        cur_state = last_state
        for i in loop_seq:
            traceback_seq.append(cur_state)
            
            cur_state = pred_state_seq[(i - 1, cur_state)]

        # put the traceback sequence in the proper orientation
        traceback_seq.reverse()

        return traceback_seq.toseq(), state_path_prob

# 需要导入模块: from Bio.Seq import MutableSeq [as 别名]
# 或者: from Bio.Seq.MutableSeq import toseq [as 别名]
                for jcod in xrange(3):
                    for ai in xrange(4):
                        cod_anc[:] = conss_gene[3 * j: 3 * (j+1)]
                        # Ancestral allele, skip (we only look at propagation of MINOR alleles)
                        if alpha[ai] == cod_anc[jcod]:
                            continue
    
                        cod_new[:] = conss_gene[3 * j: 3 * (j+1)]
                        cod_new[jcod] = alpha[ai]
    
                        aftmp = aft_der_gene[:, ai, j + jcod]
                        aftmp = aftmp[(aftmp >= bins[0]) & (aftmp <= bins[-1])]
                        if not len(aftmp):
                            continue

                        if str(cod_new.toseq().translate()) != str(cod_anc.toseq().translate()):
                            nu_syn.extend(aftmp)
                        else:
                            nu_nonsyn.extend(aftmp)
    
            if len(nu_syn):
                hist_syn += np.histogram(nu_syn, bins=bins)[0]
            if len(nu_nonsyn):
                hist_nonsyn += np.histogram(nu_nonsyn, bins=bins)[0]


    # Normalize
    hist_norm = hist.copy()
    hist_norm /= hist_norm.sum()
    hist_norm /= bins[1:] - bins[:-1]

# 需要导入模块: from Bio.Seq import MutableSeq [as 别名]
# 或者: from Bio.Seq.MutableSeq import toseq [as 别名]
class TestMutableSeq(unittest.TestCase):
    def setUp(self):
        self.s = Seq.Seq("TCAAAAGGATGCATCATG", IUPAC.unambiguous_dna)
        self.mutable_s = MutableSeq("TCAAAAGGATGCATCATG", IUPAC.ambiguous_dna)

    def test_mutableseq_creation(self):
        """Test creating MutableSeqs in multiple ways"""
        mutable_s = MutableSeq("TCAAAAGGATGCATCATG", IUPAC.ambiguous_dna)
        self.assertIsInstance(mutable_s, MutableSeq, "Creating MutableSeq")

        mutable_s = self.s.tomutable()
        self.assertIsInstance(mutable_s, MutableSeq, "Converting Seq to mutable")

        array_seq = MutableSeq(array.array(array_indicator, "TCAAAAGGATGCATCATG"),
                               IUPAC.ambiguous_dna)
        self.assertIsInstance(array_seq, MutableSeq, "Creating MutableSeq using array")

    def test_repr(self):
        self.assertEqual("MutableSeq('TCAAAAGGATGCATCATG', IUPACAmbiguousDNA())",
                         repr(self.mutable_s))

    def test_truncated_repr(self):
        seq = "TCAAAAGGATGCATCATGTCAAAAGGATGCATCATGTCAAAAGGATGCATCATGTCAAAAGGA"
        expected = "MutableSeq('TCAAAAGGATGCATCATGTCAAAAGGATGCATCATGTCAAAAGGATGCATCATG...GGA', IUPACAmbiguousDNA())"
        self.assertEqual(expected, repr(MutableSeq(seq, IUPAC.ambiguous_dna)))

    def test_equal_comparison(self):
        """Test __eq__ comparison method"""
        self.assertEqual(self.mutable_s, "TCAAAAGGATGCATCATG")

    def test_equal_comparison_of_incompatible_alphabets(self):
        with warnings.catch_warnings(record=True):
            self.mutable_s == MutableSeq('UCAAAAGGA', IUPAC.ambiguous_rna)

    def test_not_equal_comparison(self):
        """Test __ne__ comparison method"""
        self.assertNotEqual(self.mutable_s, "other thing")

    def test_less_than_comparison(self):
        """Test __lt__ comparison method"""
        self.assertTrue(self.mutable_s[:-1] < self.mutable_s)

    def test_less_than_comparison_of_incompatible_alphabets(self):
        with warnings.catch_warnings(record=True):
            self.mutable_s[:-1] < MutableSeq("UCAAAAGGAUGCAUCAUG", IUPAC.ambiguous_rna)

    def test_less_than_comparison_without_alphabet(self):
        self.assertTrue(self.mutable_s[:-1] < "TCAAAAGGATGCATCATG")

    def test_less_than_or_equal_comparison(self):
        """Test __le__ comparison method"""
        self.assertTrue(self.mutable_s[:-1] <= self.mutable_s)

    def test_less_than_or_equal_comparison_of_incompatible_alphabets(self):
        with warnings.catch_warnings(record=True):
            self.mutable_s[:-1] <= MutableSeq("UCAAAAGGAUGCAUCAUG", IUPAC.ambiguous_rna)

    def test_less_than_or_equal_comparison_without_alphabet(self):
        self.assertTrue(self.mutable_s[:-1] <= "TCAAAAGGATGCATCATG")

    def test_add_method(self):
        """Test adding wrong type to MutableSeq"""
        with self.assertRaises(TypeError):
            self.mutable_s + 1234

    def test_radd_method(self):
        self.assertEqual("TCAAAAGGATGCATCATGTCAAAAGGATGCATCATG",
                         self.mutable_s.__radd__(self.mutable_s))

    def test_radd_method_incompatible_alphabets(self):
        with self.assertRaises(TypeError):
            self.mutable_s.__radd__(MutableSeq("UCAAAAGGA", IUPAC.ambiguous_rna))

    def test_radd_method_using_seq_object(self):
        self.assertEqual("TCAAAAGGATGCATCATGTCAAAAGGATGCATCATG",
                         self.mutable_s.__radd__(self.s))

    def test_radd_method_wrong_type(self):
        with self.assertRaises(TypeError):
            self.mutable_s.__radd__(1234)

    def test_as_string(self):
        self.assertEqual("TCAAAAGGATGCATCATG", str(self.mutable_s))

    def test_length(self):
        self.assertEqual(18, len(self.mutable_s))

    def test_converting_to_immutable(self):
        self.assertIsInstance(self.mutable_s.toseq(), Seq.Seq)

    def test_first_nucleotide(self):
        self.assertEqual('T', self.mutable_s[0])

    def test_setting_slices(self):
        self.assertEqual(MutableSeq('CAAA', IUPAC.ambiguous_dna),
                         self.mutable_s[1:5], "Slice mutable seq")

        self.mutable_s[1:3] = "GAT"
        self.assertEqual(MutableSeq("TGATAAAGGATGCATCATG", IUPAC.ambiguous_dna),
                         self.mutable_s,
#.........这里部分代码省略.........