예제 #1
0
    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)
def validate_msa_characters(msa, legal_chars="-acgtn"):
    msa_info = SummaryInfo(msa)
    msa_letters = msa_info._get_all_letters()
    for let in msa_letters:
        if let not in legal_chars:
            return False
    return True
    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)
예제 #4
0
def calcAbundance(alignmentFile, consensusFile, abundanceFile, abundancePercentFile, verbose):
	print('Calculating the abundance matrix...')
	alignment = AlignIO.read(alignmentFile, "fasta")
	summary = SummaryInfo(alignment)
	consensusSeq = SeqIO.read(consensusFile, 'fasta')
	if (len(consensusSeq) == alignment.get_alignment_length()):
		abundanceMatrix = summary.pos_specific_score_matrix(consensusSeq)
	else:
		with open(consensusFile, "w") as f:			
			SeqIO.write(SeqRecord( summary.dumb_consensus(), id='consensus'), f, "fasta")
		abundanceMatrix = summary.pos_specific_score_matrix()
	if verbose:
		print("Abundance matrix (absolute values):")
		print(str(abundanceMatrix))
	with open(abundanceFile, 'w') as f:
		f.write(str(abundanceMatrix))
	for pos, abundance in enumerate(abundanceMatrix):
		for res, value in abundance.items():
			abundanceMatrix[pos][res] = 100.0 * float(value) / float(len(alignment))
	if verbose:
		print("Abundance matrix (percentages):")
		print(str(abundanceMatrix))
	with open(abundancePercentFile, 'w') as f:
		f.write(str(abundanceMatrix))
	print('OK')
예제 #5
0
    def test_proteins(self):
        a = MultipleSeqAlignment([
            SeqRecord(Seq("MHQAIFIYQIGYP*LKSGYIQSIRSPEYDNW-"), id="ID001"),
            SeqRecord(Seq("MH--IFIYQIGYAYLKSGYIQSIRSPEY-NW*"), id="ID002"),
            SeqRecord(Seq("MHQAIFIYQIGYPYLKSGYIQSIRSPEYDNW*"), 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
""")

        letters = IUPACData.protein_letters
        base_freq = 1.0 / len(letters)
        e_freq_table = {letter: base_freq for letter in letters}
        ic = s.information_content(e_freq_table=e_freq_table,
                                   chars_to_ignore=["-", "*"])
        self.assertAlmostEqual(ic, 133.061475107, places=6)
예제 #6
0
def add_consensus(alignment, threshold=0.9, ambiguous='-', name='consensus'):
    """Add a consensus line"""
    a = SummaryInfo(alignment)
    # cons=a.dumb_consensus(threshold, ambiguous)
    cons = a.gap_consensus(threshold, ambiguous)
    alignment.extend([SeqRecord(cons, id=name, name=name)])
    return alignment
예제 #7
0
def trim_aln_to_seq(alignment, sequence):
    """Trim alignment to a sequence, i.e. leave only postions that correspond to this sequence
    Note that seqeuence should be incorportatable into alignment without additional gaps in alignment.
    """

    n1 = str(uuid.uuid4())
    n2 = str(uuid.uuid4())
    #Get consensus
    a = SummaryInfo(alignment)
    cons = a.dumb_consensus(threshold=0.1, ambiguous='X')
    #Needle it
    SeqIO.write([SeqRecord(cons, id='CONS', name='CONS')], n1 + '.fasta',
                'fasta')
    SeqIO.write([SeqRecord(sequence, id='KEY', name='KEY')], n2 + '.fasta',
                'fasta')

    #Now we will redo it with Needlman Wunsh - the global alignment
    needle_cline = NeedleCommandline(asequence=n1 + ".fasta",
                                     bsequence=n2 + ".fasta",
                                     gapopen=10,
                                     gapextend=0.5,
                                     outfile=n1 + ".txt")
    stdout, stderr = needle_cline()
    # print('Needle alignment')

    align = AlignIO.read(n1 + ".txt", "emboss")
    os.system('rm %s.fasta %s.fasta %s.txt' % (n1, n2, n1))
    # print align
    # print alignment
    align.extend(alignment)
    a = align[1:, :]

    return trim_aln_to_key_seq(a, sequence)[1:, :]
예제 #8
0
def trim_aln_to_seq_length(alignment, sequence):
    """Trim alignment to a sequence, i.e. leave only postions that correspond to this sequence span"""

    n1 = str(uuid.uuid4())
    n2 = str(uuid.uuid4())
    #Get consensus
    a = SummaryInfo(alignment)
    cons = a.dumb_consensus(threshold=0.1, ambiguous='X')
    #Needle it
    SeqIO.write([SeqRecord(cons, id='CONS', name='CONS')], n1 + '.fasta',
                'fasta')
    SeqIO.write([SeqRecord(sequence, id='KEY', name='KEY')], n2 + '.fasta',
                'fasta')

    #Now we will redo it with Needlman Wunsh - the global alignment
    needle_cline = NeedleCommandline(asequence=n1 + ".fasta",
                                     bsequence=n2 + ".fasta",
                                     gapopen=10,
                                     gapextend=0.5,
                                     outfile=n1 + ".txt")
    stdout, stderr = needle_cline()
    # print('Needle alignment')

    align = AlignIO.read(n1 + ".txt", "emboss")
    os.system('rm %s.fasta %s.fasta %s.txt' % (n1, n2, n1))
    # print align
    # print alignment
    #first seq is consensus, we need to get borders useing second one.
    seq = str(align[1, :].seq)
    # print seq
    begin = seq.index(str(sequence[0]))
    end = len(seq) - seq[::-1].index(str(sequence[-1]))
    print begin
    print end
    return alignment[:, begin:end]
예제 #9
0
    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")

        c = summary.gap_consensus(ambiguous="N")
        self.assertEqual(str(c), "NNNNNNNN")

        expected = {"A": 0.25, "G": 0.25, "T": 0.25, "C": 0.25}

        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)
예제 #10
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파일: hmmer.py 프로젝트: yotamfr/prot2vec
def _run_hmmer_single_thread(sequences, iter=3):
    pbar = tqdm(range(len(sequences)), desc="sequences processed")
    database = "data/Uniprot/uniprot_sprot.fasta"
    outpth = os.path.join(tmp_dir, "align.sto")
    for seqid, seq in sequences.items():
        pbar.update(1)
        cline = "jackhmmer -N %d --acc --noali -A %s - %s" % (iter, outpth, database)

        child = subprocess.Popen(cline,
                                 stdin=subprocess.PIPE,
                                 stdout=subprocess.PIPE,
                                 stderr=subprocess.PIPE,
                                 universal_newlines=True,
                                 shell=(sys.platform != "win32"))

        stdout, _ = child.communicate(input=">%s\n%s" % (seqid, seq))
        assert child.returncode == 0

        info = SummaryInfo(AlignIO.read(outpth, "stockholm"))
        pssm = info.pos_specific_score_matrix(chars_to_ignore=[GAP])

        db.pssm.update_one({
            "_id": seqid}, {
            '$set': {"pssm": pssm.pssm, "seq": seq}
        }, upsert=True)

    pbar.close()
예제 #11
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def get_hist_ss_in_aln_as_string(alignment, type='Unknown', debug=0):
    """ gets an annotation line as a string, borrowed from aln2html"""
    sinfo = SummaryInfo(alignment)
    cons = sinfo.gap_consensus(threshold=0.9, ambiguous='X')
    features = get_hist_ss_in_aln_for_html(alignment, type=type, debug=0)
    f_description = ''
    annot_line = [0, 1, 2]
    if (features):
        annot_line[0] = list(' ' * len(cons))
        annot_line[1] = list(' ' * len(cons))
        annot_line[2] = list(' ' * len(cons))
    keys = sorted(list(features.keys()), key=lambda x: x[0])
    for k in keys:
        if (features[k].get('description', 0)):
            f_description += '{0}-{1};'.format(features[k]['symbol'],
                                               features[k]['description'])
        lev = features[k].get('level', 0)
        if (re.match('^\s+$', ''.join(annot_line[lev][k[0]:k[1] + 1]))):
            annot_line[lev][k[0]:k[1] +
                            1] = features[k]['symbol'] * (k[1] - k[0] + 1)
        else:
            lev += 1
            if (re.match('^\s+$', ''.join(annot_line[lev][k[0]:k[1] + 1]))):
                annot_line[lev][k[0]:k[1] +
                                1] = features[k]['symbol'] * (k[1] - k[0] + 1)
            else:
                lev += 1
                if (re.match('^\s+$',
                             ''.join(annot_line[lev][k[0]:k[1] + 1]))):
                    annot_line[lev][k[0]:k[1] +
                                    1] = features[k]['symbol'] * (k[1] - k[0] +
                                                                  1)

    return annot_line[0]  #other are ignored currently
예제 #12
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def get_features_in_aln(alignment, variant, save_dir="", save_gff=True):
    #Let's extract consensus
    a = SummaryInfo(alignment)
    cons = a.dumb_consensus(threshold=0.1, ambiguous='X')
    seq = Sequence(id="Consensus",
                   variant_id=variant,
                   taxonomy_id=1,
                   sequence=str(cons))
    updated_features = get_variant_features(seq,
                                            save_dir=save_dir,
                                            save_gff=save_gff)
    return updated_features
예제 #13
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def trim_aln_gaps(alignment, threshold=0.8):
    """Removes positions with more than threshold gaps in alignment"""
    a = SummaryInfo(alignment)
    cons = a.gap_consensus(threshold=threshold, ambiguous='X')
    new_aln = alignment[:, 0:0]
    for c, i in zip(cons, range(len(cons))):
        if (c == '-'):
            continue
        else:
            new_aln += alignment[:, i:i + 1]

    return new_aln
예제 #14
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def get_hist_ss_in_aln(alignment, type='Unknown', debug=0):
    """Returns sequence elements in histone alignment, all numbers assume first element in seq has number 0!!! Not like in PDB"""

    #Let's extract consensus
    if (debug):
        print(alignment)
    a = SummaryInfo(alignment)
    cons = a.gap_consensus(threshold=0.5, ambiguous='X')
    cons = Seq(str(cons).replace('-', 'X'))
    if (debug):
        print("Consensus")
        print(cons)
    hv, ss = get_hist_ss(cons, type, debug)
    return hv, ss
예제 #15
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def make_consensus(alignment_file, format_alignment):
    from Bio import AlignIO
    from Bio.Align.AlignInfo import SummaryInfo
    format = format_alignment
    alignment = AlignIO.read(alignment_file, format)
    summary = SummaryInfo(alignment)
    mat = summary.pos_specific_score_matrix()
    consensus = ""
    l = list()
    for row in mat:
        key, freq = max(row.items(), key=operator.itemgetter(1))
        consensus = "".join([consensus, key])
        l.append(freq / len(alignment._records))
    consensus = consensus.strip("-")
    return consensus, l
예제 #16
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    def test_iupac_consensus(self):
        filename = "GFF/multi_1.fna"
        format = "fasta"
        alignment = AlignIO.read(filename, format, alphabet=unambiguous_dna)
        summary = SummaryInfo(alignment)

        c = summary.iupac_consensus(ambiguous="N")
        self.assertEqual(str(c), 'TAWSRWYSNBATCG')
        self.assertNotEqual(c.alphabet, unambiguous_dna)
        self.assertTrue(isinstance(c.alphabet, DNAAlphabet))

        c = summary.gap_iupac_consensus(ambiguous="N")
        self.assertEqual(str(c), 'TAWSRWYSNBATCG')
        self.assertNotEqual(c.alphabet, unambiguous_dna)
        self.assertTrue(isinstance(c.alphabet, DNAAlphabet))
예제 #17
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 def getConservedDomain(self):
     cons = []
     align = SummaryInfo(self.__alignment)
     consenso = str(align.gap_consensus())
     temp = ''
     for i in range(len(consenso)):
         if consenso[i] not in "X-":
             temp += consenso[i]
         else:
             if temp != '':
                 cons.append(temp)
             temp = ''
     max_cons = ''
     for i in cons:
         if len(i) > len(max_cons): max_cons = i
     return max_cons
예제 #18
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def get_seed_aln_and_features(request, seed):
    from Bio.Align import MultipleSeqAlignment
    from Bio.Align.AlignInfo import SummaryInfo

    seed_file = os.path.join(settings.STATIC_ROOT_AUX, "browse", "seeds")
    try:
        histone = Histone.objects.get(id=seed)
        seed_file = os.path.join(seed_file, "{}".format(histone.id))
    except Histone.DoesNotExist:
        try:
            variant = Variant.objects.get(id=seed)
            seed_file = os.path.join(seed_file, variant.hist_type.id, "{}".format(variant.id))
            # the default names for canonical are with underscores, so we do not need to convert back. ALEXEY, 30/12/15
            # seed_file = os.path.join(seed_file, variant.hist_type.id, "{}".format(variant.id.replace("canonical", "canonical_")))
        except Variant.DoesNotExist:
            return HttpResponseNotFound('<h1>No histone variant with name {}</h1>'.format(seed))

    download = request.GET.get("download", False) == "true"

    format = request.GET.get("format", "json")

    try:
        limit = int(request.GET.get("limit", 0))
    except ValueError:
        limit = 0

    consensus = request.GET.get("consensus", False)

    if not consensus in ["limit", "all", False]:
        consensus = "all"

    response = HttpResponse(content_type='text')

    if download:
        response['Content-Disposition'] = 'attachment; filename="{}.{}"'.format(seed, format)

    sequences = SeqIO.parse("{}.fasta".format(seed_file), "fasta")

    if consensus:
        sequences = [s for i, s in enumerate(sequences) if consensus == "all" or (consensus == "limit" and i < limit)]
        msa = MultipleSeqAlignment(sequences)
        a = SummaryInfo(msa)
        sequences.insert(0, SeqRecord(id="Consensus", description="", seq=a.dumb_consensus(threshold=0.1, ambiguous='X')))
        limit = limit+1 if limit > 0 else 0

    def limited_seqs():
        for i, seq in enumerate(sequences):
            if not consensus or consensus == "limit" or (limit > 0 and i < limit):
                yield seq

    with open("{}.gff".format(seed_file)) as feature_file:
        features = feature_file.read()

    if format == "fasta":
        SeqIO.write(limited_seqs(), response, "fasta")
    elif format == "gff":
        response.write(features)
    elif format == "pdf":
        with open("{}.pdf".format(seed_file)) as pdf:
            response.write(pdf.read())
    else:
        #Default format is json
        sequences = [{"name":seq.id, "seq":seq.seq.tostring()} for seq in limited_seqs()]
        result = {"seqs":sequences, "features":features}
        response.write(json.dumps(result))

    return response
예제 #19
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def aln2html(msa,filename,features=None,title=None,description=True,field1w=20,field2w=35):
    """ 
    This function outputs HTML from msa and annotates features.
    msa - Biopython MSA,
    filename - html file to output the result.
    features - a dictionary of features, organized as follows:
    {(begin,end):{'level':0(default),'symbol':'H','description':'desc'}}
    if features overlap and not levels, they will be split to different levels.
    Only three levels (0,1,2) are available.
    """


    style="""
pre,td{margin: 0px;padding: 0px;border: 0px;}
.pos{color:blue;}
.neg{color:red;}
.pol{color:green;}
.hphob{color:grey;}
.def{color:black;}
.conserved{background:lightblue;}
.nonconserved{background:white;}
"""
    sinfo=SummaryInfo(msa)
    cons=sinfo.gap_consensus(threshold=0.9, ambiguous='X')
    
    #Let's work on features
    f_description=''
    msatext=''
    annot_line=[0,1,2]
    if(features):
        annot_line[0]=list(' '*len(cons))
        annot_line[1]=list(' '*len(cons))
        annot_line[2]=list(' '*len(cons))
        keys=sorted(list(features.keys()),key=lambda x: x[0])
        for k in keys:
            if(features[k].get('description',0)):
                f_description+='{0}-{1};'.format(features[k]['symbol'],features[k]['description'])
            lev=features[k].get('level',0)
            if(re.match('^\s+$',''.join(annot_line[lev][k[0]:k[1]+1]))):
                annot_line[lev][k[0]:k[1]+1]=features[k]['symbol']*(k[1]-k[0]+1)
            else:
                lev+=1
                if(re.match('^\s+$',''.join(annot_line[lev][k[0]:k[1]+1]))):
                    annot_line[lev][k[0]:k[1]+1]=features[k]['symbol']*(k[1]-k[0]+1)
                else:
                    lev+=1
                    if(re.match('^\s+$',''.join(annot_line[lev][k[0]:k[1]+1]))):
                        annot_line[lev][k[0]:k[1]+1]=features[k]['symbol']*(k[1]-k[0]+1)
        if(not re.match('^\s+$',''.join(annot_line[2]))):
            msatext='<TR><TD><PRE>{0:<{field1w}}</PRE></TD>'.format('annotation',field1w=field1w+2)
            if(description):
                msatext+='<TD><PRE>{0:<{field2w}}</PRE></TD>'.format('level 2',field2w=field2w+2)
            for c in annot_line[2]:
                msatext+='<TD><PRE>{0}</PRE></TD>'.format(c)
            msatext+='</TR>'
        if(not re.match('^\s+$',''.join(annot_line[1]))):
            msatext='<TR><TD><PRE>{0:<{field1w}}</PRE></TD>'.format('annotation',field1w=field1w+2)
            if(description):
                msatext+='<TD><PRE>{0:<{field2w}}</PRE></TD>'.format('level 1',field2w=field2w+2)
            for c in annot_line[1]:
                msatext+='<TD><PRE>{0}</PRE></TD>'.format(c)
            msatext+='</TR>'
        if(not re.match('^\s+$',''.join(annot_line[0]))):
            msatext='<TR><TD><PRE>{0:<{field1w}}</PRE></TD>'.format('annotation',field1w=field1w+2)
            if(description):
                msatext+='<TD><PRE>{0:<{field2w}}</PRE></TD>'.format('level 0',field2w=field2w+20)
            for c in annot_line[0]:
                msatext+='<TD><PRE>{0}</PRE></TD>'.format(c)
            msatext+='</TR>'
        f_description+='<BR><BR>'



    for s in msa:
        if(re.search(r'\d\d+',s.id)):
            gi=re.search(r'(\d\d+)',s.id).group(1)
            line='<TR><TD><PRE><a href="http://www.ncbi.nlm.nih.gov/protein/?term={0}">{1:<{field1w}}</a></PRE></TD>'.format(gi,s.id[:field1w],field1w=field1w+2)
            if(description):
                line+='<TD><PRE>{0:<{field2w}}</PRE></TD>'.format(s.description[:field2w],field2w=field2w+2)
        else:
            line='<TR><TD><PRE>{0:<{field1w}}</PRE></TD>'.format(s.id[:field1w],field1w=field1w+2)
            if(description):
                line+='<TD><PRE>{0:<{field2w}}</PRE></TD>'.format(s.description[:field2w],field1w=field1w+2)
        for c,i in zip(s.seq,range(len(s.seq))):
            line+='<TD><PRE class="{0} {1}">{2}</PRE></TD>'.format(restypedict.get(c,'def'),'conserved' if c==cons[i] and c!='-' else 'nonconserved',c)
        line+='</TR>'
        msatext=msatext+line


    a=open(filename,'w')
    a.write("""
<!DOCTYPE html>
<HTML>
<HEAD>
<META http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<TITLE>MultipleSequenceAlignment</TITLE>
<style>
{style}
</style>
</HEAD>
<BODY style="background-color:white; color:black; a:link:blue; a:active:red; a:visited:purple">
{title}<BR><BR>
{features}
<TABLE style="border:0px; border-spacing:0px; background-color:white; color:black; a:link:blue; a:active:red; a:visited:purple;">

{msatext}

</TABLE>
</BODY>
</HTML>
""".format(\
title=title,\
msatext=msatext,\
style=style,\
features=f_description
))

    a.close()
예제 #20
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def get_aln_and_features(request, ids=None):
    from tools.hist_ss import get_variant_features
    from tools.L_shade_hist_aln import write_alignments
    import subprocess
    import StringIO
    from Bio.Align import MultipleSeqAlignment
    from Bio.Align.AlignInfo import SummaryInfo
    from Bio.SeqRecord import SeqRecord
    
    save_dir = os.path.join(os.path.sep, "tmp", "HistoneDB")
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)
        os.chmod(save_dir,0o777)

    if ids is None and request.method == "GET" and "id" in request.GET:
        ids = request.GET.getlist("id")
        sequences = Sequence.objects.filter(id__in=ids)
        download = False
        upload = False
    elif request.GET.get("download", False) == "true":
        download = True
        upload = False
    else:
        #Returning 'false' stops Bootstrap table
        return "false"

    if request.GET.get("upload", False) == "true":
        uploaded_sequence = request.session.get("uploaded_sequences", [])
        if len(uploaded_sequence) > 0:
            try:
                variant = Variant.objects.get(id=uploaded_sequence[0]["variant"])
            except:
                if len(sequences) > 0:
                    variant = sequences[0].variant
                else:
                    return "false"

            uploaded_sequence = Sequence(
                id=uploaded_sequence[0]["id"], 
                variant=variant,
                sequence=uploaded_sequence[0]["sequence"],
                taxonomy=Taxonomy.objects.get(name=uploaded_sequence[0]["taxonomy"]))
            upload = True
            download = False
    

    if not download:
        if len(sequences) == 0:
            return None, None
        elif len(sequences) == 1:
            #Already aligned to core histone
            seq = sequences[0]
            hist_type = seq.variant.hist_type.id
            variants = [seq.variant]
            if upload:
                sequences = [uploaded_sequence, seq]
            else:
                #let's load the corresponding canonical
                try:
                    if(("canonical" in str(seq.variant)) or ("generic" in str(seq.variant))):
                        canonical=seq
                    elif(str(seq.variant.hist_type)=="H1"):
                        canonical=Sequence.objects.filter(variant_id='generic_'+str(seq.variant.hist_type),reviewed=True,taxonomy=seq.taxonomy)[0]
                    else:
                        canonical=Sequence.objects.filter(variant_id='canonical_'+str(seq.variant.hist_type),reviewed=True,taxonomy=seq.taxonomy)[0]
                except:
                    try: #try H2A.X as a substitute for canonical
                        if(str(seq.variant.hist_type)=='H2A'):
                            canonical=Sequence.objects.filter(variant_id='H2A.X',reviewed=True,taxonomy=seq.taxonomy)[0]
                        elif(str(seq.variant.hist_type)=='H3'): #Try H3.3
                            canonical=Sequence.objects.filter(variant_id='H3.3',reviewed=True,taxonomy=seq.taxonomy)[0]
                        elif(str(seq.variant.id)=='scH1'):
                            canonical=seq
                        else:
                            raise

                    except:
                        canonical=seq #we here default not to show the sequence by simply suppling itslef - only one line will be displayed
                        #default Xenopus
                        # if(str(seq.variant.hist_type)=="H1"):
                        #     canonical = Sequence(id="0000|xenopus|generic{}".format(hist_type), sequence=str(TemplateSequence.objects.get(variant="General{}".format(hist_type)).get_sequence().seq))
                        # else:
                        #     canonical = Sequence(id="0000|xenopus|canonical{}".format(hist_type), sequence=str(TemplateSequence.objects.get(variant="General{}".format(hist_type)).get_sequence().seq))
                sequences = [canonical, seq]
            sequence_label = seq.short_description
            
        else:
            seq = sequences[0]
            variants = list(Variant.objects.filter(id__in=sequences.values_list("variant", flat=True).distinct()))
            sequence_label = "Consensus"
        
        muscle = os.path.join(os.path.dirname(sys.executable), "muscle")
        process = subprocess.Popen([muscle], stdin=subprocess.PIPE, stdout=subprocess.PIPE)
        sequences = "\n".join([s.format() for s in sequences])
        aln, error = process.communicate(sequences)
        seqFile = StringIO.StringIO()
        seqFile.write(aln)
        seqFile.seek(0)
        sequences = list(SeqIO.parse(seqFile, "fasta")) #Not in same order, but does it matter?
        msa = MultipleSeqAlignment(sequences)
        a = SummaryInfo(msa)
        cons = Sequence(id=sequence_label, variant_id=variants[0].id, taxonomy_id=1, sequence=a.dumb_consensus(threshold=0.1, ambiguous='X').tostring())

        save_dir = os.path.join(os.path.sep, "tmp", "HistoneDB")
        if not os.path.exists(save_dir):
            os.makedirs(save_dir)

        features = get_variant_features(cons, variants=variants, save_dir=save_dir)

        #A hack to avoid two canonical seqs
        unique_sequences = [sequences[0]] if len(sequences) == 2 and sequences[0].id == sequences[1].id else sequences
        # doing the Sequence.short_description work
        #Note that the gffs are also generated with the short description not
        sequences = [{"name":"QUERY" if "QUERY" in s.id else Sequence.long_to_short_description(s.id), "seq":s.seq.tostring()} for s in unique_sequences]
        # sequences = [{"name":s.id, "seq":s.seq.tostring()} for s in sequences]
        
        if sequence_label == "Consensus":
            sequences.insert(0, cons.to_dict(id=True))
            
        request.session["calculated_msa_seqs"] = sequences
        request.session["calculated_msa_features"] = features#.to_ict() if features else {}

        result = {"seqs":sequences, "features":features} #.full_gff() if features else ""}
        return JsonResponse(result, safe=False) 
    else:
        format = request.GET.get("format", "json")
        response = HttpResponse(content_type='text')
        response['Content-Disposition'] = 'attachment; filename="sequences.{}"'.format(format)

        
        sequences = request.session.get("calculated_msa_seqs", [])
        features = request.session.get("calculated_msa_features", "")
        #features = Features.from_dict(Sequence("Consensus"), features_dict) if features_dict else None

        if format == "fasta":
            for s in sequences:
                print >> response, ">{}\n{}".format(s["name"], s["seq"])
        elif format == "gff":
            response.write(features) #.full_gff() if features else "")
        elif format == "pdf":
            aln = MultipleSeqAlignment([SeqRecord(Seq(s["seq"]), id=s["name"]) for s in sequences[1:]])
            result_pdf = write_alignments(
                [aln], 
                save_dir = save_dir
            )

            with open(result_pdf) as pdf:
                response.write(pdf.read())

            #Cleanup
            os.remove(result_pdf)
        else:
            #Default format is json
            result = {"seqs":sequences, "features":features} #.full_gff() if features else ""}
            response.write(json.dumps(result))

        return response
예제 #21
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 def getConsenso(self):
     align = SummaryInfo(self.__alignment)
     return align.gap_consensus()
예제 #22
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 def getPerc(self):
     align = SummaryInfo(self.__alignment)
     return float(1 - (align.gap_consensus().count("X") +
                       align.gap_consensus().count("-")) /
                  len(str(align.gap_consensus())))