def compute_mes(interval, matrix5, matrix3, genome):
genome = pysam.FastaFile(genome)
# 5ss 3bases in exon and 6 bases in intron
# 3ss 20 bases in intron and 3 bases in exon
if interval['strand'] == '+':
seq5 = genome.fetch(interval.chrom, interval.end - 3,
interval.end + 6).upper()
seq3 = genome.fetch(interval.chrom, interval.start - 20,
interval.start + 3).upper()
else:
seq5 = reverse_complement(
genome.fetch(interval.chrom, interval.start - 6,
interval.start + 3).upper())
seq3 = reverse_complement(
genome.fetch(interval.chrom, interval.end - 3,
interval.end + 20).upper())
name_format_str = '{seq5}:{mes5}|{seq3}:{mes3}'
if set(seq5).issubset('ACGT') and set(seq3).issubset('ACGT'):
mes5 = maxent_fast.score5(seq5, matrix=matrix5)
mes3 = maxent_fast.score3(seq3, matrix=matrix3)
interval['seq5'] = seq5
interval['mes5'] = mes5
interval['seq3'] = seq3
interval['mes3'] = mes3
else:
interval['seq5'] = seq5
interval['mes5'] = 'NA'
interval['seq3'] = seq3
interval['mes3'] = 'NA'
return interval
def calculate_gc_percentage(interval, genome, extend=0):
chrom = interval['chrom']
start = int(interval['start'])
end = int(interval['end'])
strand = interval['strand']
genome = pysam.FastaFile(genome)
if strand == '+' or strand == '.':
mid_region_seq = genome.fetch(chrom, start, end).upper()
interval['GC_exon'] = seq_gc_content(mid_region_seq)
if extend != 0:
up_intron_seq = genome.fetch(chrom, start - extend, start).upper()
dn_intron_seq = genome.fetch(chrom, end, end + extend).upper()
interval['GC_up_intron'] = seq_gc_content(up_intron_seq)
interval['GC_dn_intron'] = seq_gc_content(dn_intron_seq)
else:
mid_region_seq = reverse_complement(
genome.fetch(chrom, start, end).upper())
interval['GC_exon'] = seq_gc_content(mid_region_seq)
if extend != 0:
up_intron_seq = reverse_complement(
genome.fetch(chrom, end, end + extend).upper())
dn_intron_seq = reverse_complement(
genome.fetch(chrom, start - extend, start).upper())
interval['GC_up_intron'] = seq_gc_content(up_intron_seq)
interval['GC_dn_intron'] = seq_gc_content(dn_intron_seq)
return interval
def bind_chroseq(self, refdic, gap=0, intron=False):
"""
the gap=0 and intron=False will output CDS seq
gap>0 would not work with intron=True
grp>0 and intron=False woll output exon seq seperated by "N"
:param: refdic: the reference genome
"""
# need self.exon, self.strand
# need to note that the chr_select is 0 based
# while the exon in bigg is 1 based
if self.exon is None:
self.get_exon()
seq_l = []
for n, exon_one in enumerate(self.exon):
chro = self.chrom
start, end = exon_one
_, seq = chr_select(refdic, chro, start, end)
seq_l.append(seq.upper()) # upper case for exon
if intron is False and n < len(self.exon) - 1:
seq_l.append(gap * "N")
if intron and n < len(self.intron):
intron_one = self.intron[n]
start_i, end_i = intron_one
_, seq_intron = chr_select(refdic, chro, start_i, end_i)
seq_l.append(seq_intron.lower()) # lower case for intron
seq_raw = "".join(seq_l)
if self.strand == "+":
seq_out = seq_raw
else:
seq_out = reverse_complement(seq_raw)
self.seq_chro = "".join(seq_out)
def find_reverse_palindromes(sequence):
"""Find every reverse palindromes of size >= 4, <= 12 and
return their indices and associated length within a sequence.
Args:
sequence: string, dna sequence
Returns:
list of starting indices and length of each reverse palindrome
found
"""
output = []
for i in range(len(sequence)):
for j in range(4, 13):
if i + j > len(sequence):
continue
subseq = sequence[i:i + j]
rc = reverse_complement(subseq)
if subseq == rc:
output.append((i + 1, j))
return output
def compute_possible_dna_origins(DNA, final_peptides):
"""
Given a string of DNA and a string of peptides, find the
subsets of DNA withing the String that could have encoded the peptide
(Encoding process: DNA -> RNA -> peptide)
NOTE: for each String of DNA, we have to get the reverse complement that
we know will be attached to it during transcription into RNA
:param DNA: String - The strand of DNA
:param peptides: String - The peptide produced after transcription and translation
:return: origins: Array - A list of possible DNA origins for the peptides
"""
encoders = recursive_find_rna_encoders(set(), final_peptides)
freq_dict = FrequencyDict(DNA, len(final_peptides) * 3)
res = []
for codon in encoders:
enc_dna = rna_to_dna(codon)
enc_rev = reverse_complement(enc_dna, as_string=True)
freq = freq_dict.get(enc_dna, 0)
res.extend([enc_dna] * freq)
freq = freq_dict.get(enc_rev, 0)
res.extend([enc_rev] * freq)
return res
def most_frequent_kmers(DNA, k, mutation_thresh=0, reverse=False):
"""
Returns a list of most frequent k-mers in DNA
We'll use a Priorty Queue to track each pattern
along with its frequency in the DNA.
:param DNA: String - DNA
:param k: Integer - Length of the K-mer
:param mutation_thresh: Allows for a certain number of mismatches
:return: Set - Set of most frequent K-mers
"""
freq_dict = dictionaries.FrequencyDict(DNA, k, mutation_thresh)
kmers_found = set()
current_highest_freq = 0
for kmer, frequency in freq_dict.items():
rev = reverse_complement(kmer, as_string=True)
if reverse and rev in freq_dict:
frequency += freq_dict[rev]
if frequency > current_highest_freq:
current_highest_freq = frequency
kmers_found = set([kmer])
elif frequency == current_highest_freq:
kmers_found.add(kmer)
return kmers_found
def _test_profile_split(self, sequences, length):
counts = utils.counts(sequences, length)
profile = klib.Profile(utils.as_array(counts, length))
left, right = profile.split()
assert len(left) == len(right)
assert sum(left) + sum(right) == sum(counts.values()) * 2
indices_left = {}
indices_right = {}
indices_palindrome = {}
for s, c in counts.items():
r = utils.reverse_complement(s)
if s < r:
indices_left[utils.count_index(s)] = c * 2
elif s > r:
indices_right[utils.count_index(r)] = counts[s] * 2
else:
indices_palindrome[utils.count_index(s)] = c
assert ([c for c in left if c > 0] == [
c for i, c in sorted(
list(indices_left.items()) + list(indices_palindrome.items()))
])
assert ([c for c in right if c > 0] == [
c for i, c in sorted(
list(indices_right.items()) + list(indices_palindrome.items()))
])
def main(input_str):
"""
Main function takes string input and returns the best results depending
on scoring. Single result include sh-miR sequence,
score and link to 2D structure from mfold program
"""
sequence = check_input(input_str)
seq1, seq2, shift_left, shift_right = sequence
if not seq2:
seq2 = reverse_complement(seq1)
all_frames = get_all()
if 'error' in all_frames: #database error handler
return all_frames
frames = get_frames(seq1, seq2, shift_left, shift_right, all_frames)
original_frames = [Backbone(**elem) for elem in all_frames]
frames_with_score = []
for frame_tuple, original in zip(frames, original_frames):
score = 0
frame, insert1, insert2 = frame_tuple
mfold_data = mfold(frame.template(insert1, insert2))
if 'error' in mfold_data:
return mfold_data
pdf, ss = mfold_data[0], mfold_data[1]
score += score_frame(frame_tuple, ss, original)
score += score_homogeneity(original)
score += two_same_strands_score(seq1, original)
frames_with_score.append((score, frame.template(insert1, insert2), frame.name, pdf))
sorted_frames = [elem for elem in sorted(frames_with_score,\
key=lambda x: x[0], reverse=True) if elem[0] > 60]
return {'result': sorted_frames[:3]}
def main(fasta_block):
fasta_dict = fasta_breakup(fasta_block)
dna = fasta_dict.values()[0]
restriction_sites = []
for block_len in range(4,14,2):
idx = 0
while idx <= len(dna) - block_len:
current = dna[0+idx: block_len+idx]
# split current in half
if current[:(block_len/2)] == "".join(reverse_complement(current[(block_len/2):])):
print "palindrome, yo", current, current[:(block_len/2)], "".join(reverse_complement(current[(block_len/2):]))
restriction_sites.append([idx+1, block_len])
idx += 1
print restriction_sites
return restriction_sites
def d_duval_(seq, alg, **kwargs):
factors1 = [len(i) for i in alg(seq, **kwargs)]
complement = reverse_complement(seq)
factors2 = [len(i) for i in reversed(alg(complement, **kwargs))]
rest = seq; result = []
while factors1 and factors2:
if factors1[0] < factors2[0]:
n = factors1.pop(0)
factors2[0] = factors2[0] - n
if factors2[0] == 0: factors2.pop(0)
else:
n = factors2.pop(0)
factors1[0] = factors1[0] - n
if factors1[0] == 0: factors1.pop(0)
f, rest = rest[:n], rest[n:]
result.append(f)
while factors1:
n = factors1.pop(0)
f, rest = rest[:n], rest[n:]
result.append(f)
while factors2:
n = factors2.pop(0)
f, rest = rest[:n], rest[n:]
result.append(f)
return result
def test_profile_reverse_complement_palindrome(self):
counts = utils.counts(['ACCTAGGT'], 8)
profile = klib.Profile(utils.as_array(counts, 8))
for i in range(profile.length):
assert (profile.binary_to_dna(profile.reverse_complement(i)) ==
utils.reverse_complement(profile.binary_to_dna(i)))
def get_adenylation_domains(fasta, known=None, lagging_strand=False):
adenylation_domains = []
fasta_seqs = []
for fs in SeqIO.parse(fasta, 'fasta'):
revcom=False
seq = str(fs.seq)
pepseq, rf = get_pepseq(seq)
if rf < 0 == lagging_strand:
revcom=True
seq = utils.reverse_complement(seq)
fasta_seqs.append({'id': fs.id, 'seq': seq, 'pepseq': pepseq, 'rf': rf})
for fs in fasta_seqs:
utils.run_cmd([hmmsearch, '--domtblout', 'dump', os.path.abspath('lib/AMP-binding.hmm'), '-'],
'>header\n' + pepseq)
with open('dump') as f:
out = f.read()
res_stream = StringIO(out)
os.remove('dump')
results = list(SearchIO.parse(res_stream, 'hmmsearch3-domtab'))
for result in results:
for i, hsp in enumerate(result.hsps, 1):
s = hsp.hit_start
e = hsp.hit_end
adenylation_domains.append((AdenylationDomain(fs['seq'][s*3:e*3], known, '{}_{}'.format(fs['id'], i), revcom), s, e))
return adenylation_domains
def test_profile_reverse_complement(self):
counts = utils.counts(utils.SEQUENCES, 8)
profile = klib.Profile(utils.as_array(counts, 8))
for i in range(profile.length):
assert (profile.binary_to_dna(profile.reverse_complement(i)) ==
utils.reverse_complement(profile.binary_to_dna(i)))
def _test_profile_split(self, sequences, length):
counts = utils.counts(sequences, length)
profile = klib.Profile(utils.as_array(counts, length))
left, right = profile.split()
assert len(left) == len(right)
assert sum(left) + sum(right) == sum(counts.values()) * 2
indices_left = {}
indices_right = {}
indices_palindrome = {}
for s, c in counts.items():
r = utils.reverse_complement(s)
if s < r:
indices_left[utils.count_index(s)] = c * 2
elif s > r:
indices_right[utils.count_index(r)] = counts[s] * 2
else:
indices_palindrome[utils.count_index(s)] = c
assert ([c for c in left if c > 0] ==
[c for i, c in sorted(list(indices_left.items()) +
list(indices_palindrome.items()))])
assert ([c for c in right if c > 0] ==
[c for i, c in sorted(list(indices_right.items()) +
list(indices_palindrome.items()))])
def main(input_str):
"""
Main function takes string input and returns the best results depending
on scoring. Single result include sh-miR sequence,
score and link to 2D structure from mfold program
"""
sequence = check_input(input_str)
seq1, seq2, shift_left, shift_right = sequence
if not seq2:
seq2 = reverse_complement(seq1)
all_frames = get_all()
if 'error' in all_frames: #database error handler
return all_frames
frames = get_frames(seq1, seq2, shift_left, shift_right, all_frames)
original_frames = [Backbone(**elem) for elem in all_frames]
frames_with_score = []
for frame_tuple, original in zip(frames, original_frames):
score = 0
frame, insert1, insert2 = frame_tuple
mfold_data = mfold(frame.template(insert1, insert2))
if 'error' in mfold_data:
return mfold_data
pdf, ss = mfold_data[0], mfold_data[1]
score += score_frame(frame_tuple, ss, original)
score += score_homogeneity(original)
score += two_same_strands_score(seq1, original)
frames_with_score.append(
(score, frame.template(insert1, insert2), frame.name, pdf))
sorted_frames = [elem for elem in sorted(frames_with_score,\
key=lambda x: x[0], reverse=True) if elem[0] > 60]
return {'result': sorted_frames[:3]}
def test_profile_reverse_complement_palindrome(self):
counts = utils.counts(['ACCTAGGT'], 8)
profile = klib.Profile(utils.as_array(counts, 8))
for i in range(profile.length):
assert (profile.binary_to_dna(
profile.reverse_complement(i)) == utils.reverse_complement(
profile.binary_to_dna(i)))
def test_profile_balance_palindrome(self):
counts = utils.counts(['AATT'], 4)
profile = klib.Profile(utils.as_array(counts, 4))
profile.balance()
counts.update(dict((utils.reverse_complement(s), c)
for s, c in counts.items()))
utils.test_profile(profile, counts, 4)
def test_profile_balance(self):
counts = utils.counts(utils.SEQUENCES, 8)
profile = klib.Profile(utils.as_array(counts, 8))
profile.balance()
counts.update(dict((utils.reverse_complement(s), c)
for s, c in counts.items()))
utils.test_profile(profile, counts, 8)
def test_profile_balance_palindrome(self):
counts = utils.counts(['AATT'], 4)
profile = klib.Profile(utils.as_array(counts, 4))
profile.balance()
counts.update(
dict((utils.reverse_complement(s), c) for s, c in counts.items()))
utils.test_profile(profile, counts, 4)
def test_profile_reverse_complement(self):
counts = utils.counts(utils.SEQUENCES, 8)
profile = klib.Profile(utils.as_array(counts, 8))
for i in range(profile.length):
assert (profile.binary_to_dna(
profile.reverse_complement(i)) == utils.reverse_complement(
profile.binary_to_dna(i)))
def test_profile_balance(self):
counts = utils.counts(utils.SEQUENCES, 8)
profile = klib.Profile(utils.as_array(counts, 8))
profile.balance()
counts.update(
dict((utils.reverse_complement(s), c) for s, c in counts.items()))
utils.test_profile(profile, counts, 8)
def split_assembled_genome(gtf_path, fasta_path, od='.', L=98):
trs = parse_gtf(gtf_path)
print('GTF parsed')
scaffolds = parse_fasta(fasta_path)
print('Scaffolds parsed')
wrong_scaffolds = 0
for tr, data in trs.items():
processed = []
unprocessed = []
if data['scaffold'] in scaffolds:
sequence = scaffolds[data['scaffold']]
else:
print(f'{data["scaffold"]} not in FASTA file {fasta_path}')
wrong_scaffolds += 1
continue
for exon in data['exons']:
processed.append((int(exon['start']) - 1, int(exon['end']) - 1))
unprocessed.append(
(int(exon['start']) - L, int(exon['end']) + L - 2))
processed = list(merge_intervals(processed))
unprocessed = merge_intervals(unprocessed)
processed = ''.join(map(lambda iv: sequence[iv[0]:iv[1]], processed))
splice_junctions = []
for iv in unprocessed:
if iv[1] - iv[0] < 3 * L - 3:
# If the length of the exon is < L-1
splice_junctions.append(iv)
else:
splice_junctions.append((iv[0], iv[0] + 2 * L - 2))
splice_junctions.append((iv[1] - (2 * L) + 2, iv[1]))
splice_junctions = [
collapse_N(sequence[iv[0]:iv[1]].upper())
for iv in splice_junctions
]
processed = collapse_N(processed).upper()
if data['strand'] == '-':
processed = reverse_complement(processed)
splice_junctions = map(reverse_complement, splice_junctions)
with open(f'{od}/processed_transcripts.fasta', 'a') as fh:
fh.write(f'>{tr}\n')
fh.write('\n'.join(
[processed[i:i + 80] for i in range(0, len(processed), 80)]))
fh.write('\n')
with open(f'{od}/splice_junctions.fasta', 'a') as fh:
for i, sj in enumerate(splice_junctions):
fh.write(f'>{tr}:{i}\n')
fh.write(f'{sj}\n')
print('DONE!')
print(
f'{wrong_scaffolds} scaffolds were not found, and the corresponding annotations were ignored.'
)
def get_sequences(self, locations, width=200):
# need to ensure that most locations on the forward
# and reverse strands are mappable
seqs = [utils.makestr(self.genome[loc[0]][int(loc[1])-width/2:int(loc[1])+width/2]) if loc[3]=='+' \
else utils.reverse_complement(utils.makestr(self.genome[loc[0]][int(loc[2])-width/2+1:int(loc[2])+width/2+1])) \
for loc in locations]
return seqs
def get_sequences(self, locations, width=200):
# need to ensure that most locations on the forward
# and reverse strands are mappable
seqs = [utils.makestr(self.genome[loc[0]][int(loc[1])-width/2:int(loc[1])+width/2]) if loc[3]=='+' \
else utils.reverse_complement(utils.makestr(self.genome[loc[0]][int(loc[2])-width/2+1:int(loc[2])+width/2+1])) \
for loc in locations]
return seqs
def test_balance(self):
counts = utils.counts(utils.SEQUENCES, 8)
filename = self.empty()
with utils.open_profile(self.profile(counts, 8)) as input_handle:
with utils.open_profile(filename, 'w') as output_handle:
kmer.balance(input_handle, output_handle)
counts.update(dict((utils.reverse_complement(s), c) for s, c in counts.items()))
utils.test_profile_file(filename, counts, 8)
def test_balance(self):
counts = utils.counts(utils.SEQUENCES, 8)
filename = self.empty()
with utils.open_profile(self.profile(counts, 8)) as input_handle:
with utils.open_profile(filename, 'w') as output_handle:
kmer.balance(input_handle, output_handle)
counts.update(dict((utils.reverse_complement(s), c) for s, c in counts.items()))
utils.test_profile_file(filename, counts, 8)
def getSeq(self,g):
seq = ''
for node in self.nodeIds:
nodeSeq = g.nodes[abs(node)].nodeSeq
if node < 0:
nodeSeq = reverse_complement(nodeSeq)
if seq != '':
nodeSeq = nodeSeq[g.overlap:]
seq = seq + nodeSeq
return(seq)
def frequent_words_with_mismatches_with_revcomp(text: str, k: int,
d: int) -> Set[str]:
"""
>>> frequent_words_with_mismatches_with_revcomp("ACGTTGCATGTCGCATGATGCATGAGAGCT", 4, 1) == {'ATGT', 'ACAT'}
True
"""
freq_map = _frequent_words_helper(text, k, d)
freq_map_rc = _frequent_words_helper(reverse_complement(text), k, d)
freq_map += freq_map_rc
_, maxval = freq_map.most_common(1)[0]
res = {k for k, v in freq_map.items() if v == maxval}
return res
def find_wells():
mm = mismatch_reporters()
ls = load_seqs()
successes = {}
for k,v in mm.iteritems():
if not 'fwd' in k: continue
successes[k] = [ (k1,v1)
for k1,v1 in ls.iteritems()
if v[6:-1] in zutils.reverse_complement(v1)]
print
print
print
print ' '+'\n '.join(sorted([str((k,len([elt[0] for elt in v]),[elt[0] for elt in v])) for k,v in successes.iteritems()]))
return successes
def get_key_factor(factors, normalize=False):
if len(factors) > 3:
longest = max(factors[1:-1], key=lambda factor: len(factor))
else:
longest = max(factors, key=lambda factor: len(factor))
if normalize:
reverse = reverse_complement(longest)
if reverse < longest:
longest = reverse
if 10 < len(longest) < 20:
longest = longest[:10] + longest[-(len(longest) - 10):]
elif len(longest) > 20:
longest = longest[:10] + longest[-10:]
return longest
def main(fasta_block):
dna_dict = fasta_breakup(fasta_block)
dna_target = dna_dict.values()[0]
rna_target = dna_to_rna(dna_target)
rev_comp_dna = "".join(reverse_complement(dna_target))
rev_comp_rna = dna_to_rna(rev_comp_dna)
protein_strings = []
for start in find_all(rna_target, 'AUG'):
protein_strings.append(translate(start, rna_target))
for start in find_all(rev_comp_rna, 'AUG'):
protein_strings.append(translate(start, rev_comp_rna))
#dedupe and remove Nones
protein_strings = [prot for prot in protein_strings if prot != None]
cleaned_proteins = list(set(protein_strings))
return "\n".join(cleaned_proteins)
def make_oligos(self):
if not self.library:
print 'Must have mutation library before making oligos.'
return
for aa in self.library:
seq = self.nt_seq
for s, e, c in [(res[1], res[2], dgn) for res, dgn, tf in zip(self.code, self.library[aa]['dgn'], self.library[aa]['cdns']) if tf]:
# TODO: Codon usage..
c = ''.join([next(iter(utils.dgn_to_nts[nt])) for nt in c])
seq = seq[:s] + c + seq[e:]
oligo_positions = []
mut_positions = []
self.oligos[aa] = []
for s in self.library[aa]['oligo_set']:
mut_start = s[0]*3
mut_end = s[-1]*3+3
mut = seq[mut_start:mut_end]
pre_len = (oligo_length - len(mut)) / 2
oligo_start = mut_start-pre_len
oligo_end = oligo_start + oligo_length
oligo_seq = self.nt_seq[oligo_start:mut_start] + mut.lower() + self.nt_seq[mut_end:oligo_end]
if self.revcom:
oligo_seq = utils.reverse_complement(oligo_seq)
self.oligos[aa].append(oligo_seq)
oligo_positions.append([oligo_start, oligo_end])
mut_positions.append([mut_start, mut_end])
for i, pos in enumerate(oligo_positions[1:]):
if mut_positions[i][1] > pos[0]:
print 'Oligo clash detected..'
return
def mismatch_reporters():
enzymes = {'fwd': ['CTAGA', 'G'],
'rev': ['GATCC', 'T']}
primers, names = [], []
for k, r in reporters.iteritems():
for m_ct in 0, 2, 3:
if m_ct > 0: nts = [n for n in nt_list if n!=ids[k]]
else: nts = [ids[k]]
for mm in nts:
rep = re.sub('N', mm ,r, m_ct)
rep = re.sub('N', ids[k], rep)
fwd = list(enzymes['fwd'])
fwd.insert(1,rep)
rev = list(enzymes['rev'])
rev.insert(1,zutils.reverse_complement(rep))
names.append('{0}_{1}mm={2}_rev'.format(k, m_ct, mm))
primers.append(''.join(rev))
names.append('{0}_{1}mm={2}_fwd'.format(k, m_ct, mm))
primers.append(''.join(fwd))
return dict([(n,p) for n,p in zip(names, primers)])
def locate_breakpoint(input_file, output_file, reference_file, margin=200):
class Breakpoint_locator(object):
def __init__(self):
self.consensus = None
self.seq_around_bp = None
self.seq_start = None
self.seq_end = None
self.seq_dir = None
self.bp_pos_consensus = None
self.bp_pos_reference = None
self.tmp_dir = tempfile.mkdtemp()
def __del__(self):
shutil.rmtree(self.tmp_dir)
def initialize(self, cluster_id, consensus, seq_around_bp, seq_start,
seq_end, seq_dir):
self.cluster_id = cluster_id
self.consensus = consensus
self.seq_around_bp = seq_around_bp
self.seq_start = seq_start
self.seq_end = seq_end
self.seq_dir = seq_dir
self.bp_pos_consensus = None
self.bp_pos_reference = None
if len(self.consensus) >= 1000:
self.consensus = self.consensus[:1000]
def locate_by_alignment(self):
with open(self.tmp_dir + '/' + self.cluster_id + ".query.fa",
'w') as hout:
print(">query_%s\n%s" % (self.cluster_id, self.seq_around_bp),
file=hout)
with open(self.tmp_dir + '/' + self.cluster_id + ".target.fa",
'w') as hout:
print(">target_%s\n%s" % (self.cluster_id, self.consensus),
file=hout)
alignment_info = nanomonsv.long_read_validate.ssw_check(
self.tmp_dir + '/' + self.cluster_id + ".target.fa",
self.tmp_dir + '/' + self.cluster_id + ".query.fa")
# print(self.seq_start, self.seq_end, self.seq_dir)
# print(alignment_info["query_" + self.cluster_id])
if "query_" + self.cluster_id not in alignment_info: return
_, tstart_a, tend_a, qstart_a, qend_a, strand_a = alignment_info[
"query_" + self.cluster_id]
if strand_a != '+': return
self.bp_pos_consensus = tend_a
if self.seq_dir == '+':
self.bp_pos_reference = self.seq_end - (
len(self.seq_around_bp) - qend_a)
else:
self.bp_pos_reference = self.seq_start + (
len(self.seq_around_bp) - qend_a)
# print(self.bp_pos_reference, self.bp_pos_consensus)
bp_loc = Breakpoint_locator()
fasta_file = pysam.FastaFile(reference_file)
with open(input_file, 'r') as hin, open(output_file, 'w') as hout:
for row in csv.reader(hin, delimiter='\t'):
if row[4] == '+':
seq_around_bp = fasta_file.fetch(row[1],
int(row[2]) - margin,
int(row[3]))
bp_loc.initialize(row[0], row[5], seq_around_bp,
int(row[2]) - margin + 1, int(row[3]), '+')
else:
seq_around_bp = fasta_file.fetch(row[1], int(row[2]),
int(row[3]) + margin)
seq_around_bp = reverse_complement(seq_around_bp)
bp_loc.initialize(row[0], row[5], seq_around_bp,
int(row[2]) + 1,
int(row[3]) + margin, '-')
bp_loc.locate_by_alignment()
if bp_loc.bp_pos_reference is not None:
print("%s\t%s\t%d\t%s\t%s" %
(row[0], row[1], bp_loc.bp_pos_reference, row[4],
row[5][bp_loc.bp_pos_consensus:]),
file=hout)
del bp_loc
fasta_file.close()
def get_node_seq(self, nodeId):
if nodeId < 0:
nodeSeq = reverse_complement(self.nodes[-nodeId].nodeSeq.strip())
else:
nodeSeq = self.nodes[nodeId].nodeSeq.strip()
return (nodeSeq)
def SNV_main(args, mut_df=None, frameshift_df=None):
opts = vars(args)
############################
# read in necessary files
############################
# read in position to trinucleotide file
logger.info('reading pos_to_nuc_dictionary...')
# read in data frame
pos_to_nuc_df = pd.read_table('db/merged_pos_to_context_class_final.txt',
sep='\t',
names=('pos', 'trinucleotide',
'coefficient'))
if len(pos_to_nuc_df[
pos_to_nuc_df['trinucleotide'].astype(str).str.len() != 4]) != 0:
logger.info('something is wrong with reading pos_to_nuc_dictionary...')
sys.exit()
# make dictionary
pos_to_nuc = pos_to_nuc_df.set_index('pos')['trinucleotide'].to_dict()
pos_to_nuc_keys = pos_to_nuc.keys()
# read in trinucleotide to position file
logger.info('reading nuc_to_pos_dictionary...')
nuc_to_pos_dict = {}
nuc_to_cumsum_dict = {}
for k in strand_trinucs:
df = pd.read_table('db/' + k + '_data.txt',
sep='\t',
names=('pos', 'trinucleotide', 'coefficient'))
nuc_to_pos_dict[k] = df['pos'].values
p = df['coefficient'].values
cdf = np.cumsum(p)
cdf /= cdf[-1]
nuc_to_cumsum_dict[k] = cdf
# read in cancer gene file
logger.info('reading cancer_gene_dictionary...')
pos_to_codon_dict = {}
pos_to_gene_dict = {}
tmp_Chr_pos = ''
with open('db/cancergene_pos_list_final.txt', 'r') as hin:
for line in hin:
F = line.rstrip('\n').split(' ')
if len(F) < 4:
continue
if opts['gene'] and F[0] != opts['gene']:
continue
if len(F[3]) != 3 | len(F[3]) != 11:
logger.info("codon frame error...: {0}, {1}, {2}".format(
F[0], F[1], F[3]))
sys.exit()
pos_to_gene_dict.setdefault(F[1], []).append(F[0])
pos_to_codon_dict.setdefault(F[1], []).append(';'.join(
[F[2], F[3], F[4]]))
hin.close()
pos_to_gene_dict_keys = pos_to_gene_dict.keys()
#############################
# modify mutation dataframe
#############################
# get mutation df
mut_df = pd.read_csv(opts['maf_file'], sep='\t')
orig_num_mut = len(mut_df)
# rename columns to fit my internal column names
rename_dict = {
'Hugo_Symbol': 'Gene',
'Tumor_Sample_Barcode': 'Tumor_Sample',
'Tumor_Seq_Allele2': 'Tumor_Allele',
'Tumor_Seq_Allele': 'Tumor_Allele',
}
mut_df.rename(columns=rename_dict, inplace=True)
# drop rows with missing info
na_cols = [
'Gene', 'Reference_Allele', 'Tumor_Allele', 'Start_Position',
'Chromosome'
]
mut_df = mut_df.dropna(subset=na_cols)
logger.info('Kept {0} mutations after droping mutations with missing '
'information (Droped: {1})'.format(len(mut_df),
orig_num_mut - len(mut_df)))
if opts['gene']:
mut_df = gene_analysis(mut_df, opts['gene'], pos_to_nuc_keys,
opts['maf_file'])
#############################
#. SNV dataframe
#############################
# select valid single nucleotide variants only and corrects for 1-based coordinates!! (important)
snv_df = filtering.snv_mutation_df(mut_df, opts['unique'])
# get chromosome-position
snv_df['Chrom_Pos'] = snv_df['Chromosome'] + ':' + snv_df[
'Start_Position'].astype(str)
# remove SNVs of non-coding regions
orig_len = len(snv_df)
snv_df = snv_df[snv_df['Chrom_Pos'].isin(pos_to_nuc_keys)]
after_len = len(snv_df)
log_msg = ('Dropped {num_dropped} non-coding SNV mutations.'.format(
num_dropped=orig_len - after_len))
logger.info(log_msg)
#############################
#. SNV check
############################
# get trincleotide context
snv_df['trinucleotide'] = snv_df['Chrom_Pos'].apply(
lambda x: pos_to_nuc[x])
snv_df['trinucleotide'] = snv_df['trinucleotide'].astype('category')
snv_df['Chrom_Pos'] = snv_df['Chrom_Pos'].astype('category')
# check if the mutation is in the gene_list
snv_df['gene'] = snv_df['Chrom_Pos'].map(pos_to_gene_dict)
tmp_snv_df = snv_df.dropna(subset=['gene'])
outcome = []
chr_pos = tmp_snv_df.Chrom_Pos.values
t_allele = tmp_snv_df.Tumor_Allele.values
n_allele = tmp_snv_df.Reference_Allele.values
# check if the mutation is synonymous / non-synonymous / splice site
for idx in range(tmp_snv_df.shape[0]):
tmp_outcome = []
# there are genes with different reading frames
for item in pos_to_codon_dict[chr_pos[idx]]:
pos_in_codon = item.split(';')[0]
codon_seq = item.split(';')[1]
strand = item.split(';')[2]
if pos_in_codon == 'splice_site':
tmp_outcome.append('splice_site')
continue
# check if base change causes amino acid change
codon_seq_list = list(codon_seq)
if codon_seq_list[int(pos_in_codon)] == n_allele[idx]:
codon_seq_list[int(pos_in_codon)] = t_allele[idx]
elif codon_seq_list[int(pos_in_codon)] == utils.reverse_complement(
n_allele[idx]):
codon_seq_list[int(pos_in_codon)] = utils.reverse_complement(
t_allele[idx])
else:
print "error: " + chr_pos[idx] + pos_to_codon_dict[
chr_pos[idx]]
new_codon_seq = ''.join(codon_seq_list)
if codon_table[codon_seq] == codon_table[new_codon_seq]:
tmp_outcome.append('synonymous')
else:
tmp_outcome.append('non-synonymous')
outcome.append(':'.join(tmp_outcome))
tmp_snv_df['outcome'] = outcome
tmp_snv_df['original'] = 'original'
tmp_snv_df.to_csv(opts['output_prefix'] + '.final_snv_result.csv',
columns=[
'Tumor_Sample', 'original', 'Gene', 'Chromosome',
'Start_Position', 'End_Position', 'Reference_Allele',
'Tumor_Allele', 'Chrom:Pos', 'gene', 'outcome'
],
index=False)
#############################
#. SNV simulation
############################
max_num_sim = opts['simulation_number'] # number of simulations
for num_sim in range(max_num_sim):
log_msg = ('Performing simulation {num_simulation}...'.format(
num_simulation=num_sim + 1))
logger.info(log_msg)
# randomization
trinuc = snv_df['trinucleotide'].values
new_pos_list = []
for idx in range(snv_df.shape[0]):
wr = weighted_choice(nuc_to_pos_dict[trinuc[idx]],
nuc_to_cumsum_dict[trinuc[idx]])
new_pos_list.append(wr)
snv_df['New_chr_pos'] = new_pos_list
# check if new chr_pos is in gene_list
snv_df['New_gene'] = snv_df['New_chr_pos'].map(pos_to_gene_dict)
tmp_snv_df = snv_df.dropna(subset=['New_gene'])
#print tmp_snv_df
outcome = []
chr_pos = tmp_snv_df.New_chr_pos.values
t_allele = tmp_snv_df.Tumor_Allele.values
n_allele = tmp_snv_df.Reference_Allele.values
for idx in range(tmp_snv_df.shape[0]):
tmp_outcome = []
for item in pos_to_codon_dict[chr_pos[idx]]:
pos_in_codon = item.split(';')[0]
codon_seq = item.split(';')[1]
strand = item.split(';')[2]
if pos_in_codon == 'splice_site':
tmp_outcome.append('splice_site')
continue
codon_seq_list = list(codon_seq)
if codon_seq_list[int(pos_in_codon)] == n_allele[idx]:
codon_seq_list[int(pos_in_codon)] = t_allele[idx]
elif codon_seq_list[int(
pos_in_codon)] == utils.reverse_complement(
n_allele[idx]):
codon_seq_list[int(
pos_in_codon)] = utils.reverse_complement(
t_allele[idx])
else:
print "error: " + chr_pos[idx] + pos_to_codon_dict[
chr_pos[idx]]
new_codon_seq = ''.join(codon_seq_list)
if codon_table[codon_seq] == codon_table[new_codon_seq]:
tmp_outcome.append('synonymous')
else:
tmp_outcome.append('non-synonymous')
outcome.append(':'.join(tmp_outcome))
tmp_snv_df['New_outcome'] = outcome
tmp_snv_df['simulation_num'] = 'simulation' + str(int(num_sim) + 1)
tmp_snv_df.to_csv(opts['output_prefix'] + '.final_snv_result.csv',
columns=[
'Tumor_Sample', 'simulation_num', 'Gene',
'Chromosome', 'Start_Position', 'End_Position',
'Reference_Allele', 'Tumor_Allele',
'New_chr_pos', 'New_gene', 'New_outcome'
],
mode='a',
header=False,
index=False)
log_msg = ('Successfully finished. gene:{gene}, maf:{maf}'.format(
gene=opts['gene'], maf=opts['maf_file']))
logger.info(log_msg)
def context_generator(fa_file, chroms, min_length=3, max_length=5, padding=1):
""" Creates context and target k-mers using provided fasta and
fasta index file. Using a 1 base sliding window approach with
random k-mer sizes between min and max length. Both polarities
are sampled randomly.
E.g. min_length=3, max_length=5, padding=1
rnd_kmer_sizes = [4, 3, 5]
CATATCA -> ['CATA', 'ATA', 'TATCA']
-> ('chr?', 'ATA', ['CATA', 'TATCA'])
DNA sequences will be converted into ints for the final result
-> ('chr?', 12, [140, 1140])
Args:
fa_file (str): Path to fasta file with with accompanying
Samtools index file (*.fai).
chroms (list): Orded list of chromosome/parent ids which will
be included when iterating over the fasta file.
min_length (int): Minimal allowed kmer size (nt).
max_length (int): Maximum allowed kmer size (nt).
padding (int): Number of kmers, on each side, added to the context.
Yields:
chromosom_id (str), target_seq (int), list(context_seqs (ints))
"""
kmer_sizes = np.arange(min_length, max_length + 1)
with pysam.FastaFile(fa_file) as ref:
for chrom in chroms:
chr_seq = ref.fetch(chrom)
for subseq_pos in range(0, len(chr_seq)):
# Create random kmer sizes.
rnd_kmer_sizes = np.random.choice(kmer_sizes, padding * 2 + 1)
# Extract sub-sequence from provided fasta file.
subseq = chr_seq[subseq_pos:subseq_pos + rnd_kmer_sizes.size +
rnd_kmer_sizes.max()]
if len(subseq) < rnd_kmer_sizes.size + rnd_kmer_sizes.max():
continue
# Randomly use both strand for learning (Data Augmentation).
if np.random.randint(2):
subseq = reverse_complement(subseq)
try:
num_kmers = []
for i, pos in enumerate(rnd_kmer_sizes):
kmer_seq = subseq[i:i + rnd_kmer_sizes[i]]
number_seq = multisize_patten2number(
kmer_seq, min_length, max_length)
num_kmers.append(number_seq)
context = np.array(num_kmers[:padding] +
num_kmers[-padding:])
# np.random.shuffle(context)
target = num_kmers[padding]
yield chrom, target, context
except (KeyError, IndexError, ValueError): # as e:
pass # Was not able to convert patten to number or
def gencode_codon_list(target_gene, output):
gencode_df = pd.read_table('db/gencode_coding.modified.bed', names=('chr', 'start', 'end', 'ID', 'type', 'strand', 'gene', 'order', 'sum'))
gencode_df = gencode_df[gencode_df['gene'] == target_gene]
gencode_df = gencode_df[gencode_df['chr'] != 'chrY']
ID_list = gencode_df['ID'].unique()
# select only unique ID
ID_list = list(set(ID_list))
# get sequence for each refID
hin = open("db/gencode_coding.modified.bed", 'r')
gene_seq_dict = {}
for line in hin:
F = line.rstrip('\n').split('\t')
chrom = F[0].replace('chr','')
if chrom not in chroms:
continue
if F[4] != "coding":
continue
coding_start = int(F[1])
coding_end = int(F[2])
strand = F[5]
ID = F[3]
gene = F[6]
if gene != target_gene:
continue
for item in ID_list:
if item not in gene_seq_dict: gene_seq_dict[item] = ''
if ID == item:
exon_seq = fa.fetch(reference=chrom, start=coding_start, end =coding_end).upper()
gene_seq_dict[item] = gene_seq_dict[item] + exon_seq
hin.close()
hout = open(output, 'a')
# get codon information from refgene
hin = open("db/gencode_coding.modified.bed", 'r')
exon_length_dict = {}
for line in hin:
F = line.rstrip('\n').split('\t')
chrom = F[0].replace('chr','')
if chrom not in chroms:
continue
if F[4] == "coding":
coding_start = int(F[1])
coding_end = int(F[2])
strand = F[5]
ID = F[3]
gene = F[6]
if gene != target_gene:
continue
for item in ID_list:
if item != ID: continue
if item not in exon_length_dict: exon_length_dict[item] = 0
if strand == '+':
for pos in range(coding_end - coding_start):
# relative pos in the gene
pos2 = pos + exon_length_dict[item]
codon_pos = pos2 // 3
codon_start = codon_pos * 3
pos_in_codon = pos2 % 3
print >> hout, gene, item, ':'.join([chrom, str(coding_start + pos)]), pos_in_codon, gene_seq_dict[item][codon_start:(codon_start+3)], strand
exon_length_dict[item] = exon_length_dict[item] + (coding_end - coding_start)
if strand == '-':
for pos in range(coding_end - coding_start):
# relative pos in the gene
pos2 = pos + exon_length_dict[item]
codon_pos = pos2 // 3
codon_start = codon_pos * 3
pos_in_codon = pos2 % 3
print >> hout, gene, item, ':'.join([chrom, str(coding_start + pos)]), 2 - (pos_in_codon), utils.reverse_complement(gene_seq_dict[item][codon_start:(codon_start+3)]), strand
exon_length_dict[item] = exon_length_dict[item] + (coding_end - coding_start)
elif F[4] == "intron":
start = int(F[1])
end = int(F[2])
strand = F[5]
ID = F[3]
gene = F[6]
if gene != target_gene:
continue
for pos in (start, start + 1, end - 2, end - 1):
print >> hout, gene, ID, ':'.join([chrom, str(pos)]), 'splice_site', 'splice_site', strand
hin.close()
hout.close()
def count_read_by_alignment(input_file,
bam_file,
reference,
output_file,
validate_sequence_length=200,
score_ratio_thres=1.4,
start_pos_thres=0.2,
end_pos_thres=0.8):
class Alignment_counter(object):
def __init__(self, score_ratio_thres, start_pos_thres, end_pos_thres):
self.key = ''
# self.hout = open(output_file, 'w')
self.query_seq = None
self.target_seq_list = []
self.score_ratio_thres = score_ratio_thres
self.start_pos_thres = start_pos_thres
self.end_pos_thres = end_pos_thres
self.tmp_dir = tempfile.mkdtemp()
def __del__(self):
# self.hout.close()
shutil.rmtree(self.tmp_dir)
def initialize(self, key, query_seq):
self.key = key
self.query_seq = query_seq
self.target_seq_list = []
def add_query_seq(self, read_name, read_seq):
self.target_seq_list.append((read_name, read_seq))
def count_alignment(self):
if len(self.target_seq_list) == 0: return None
cluster_id, _, _, _ = key.split('\t')
"""
with open(self.tmp_dir + '/' + cluster_id + ".target.fa", 'w') as hout_ta:
for read in self.target_seq_list:
print(">%s\n%s" % (read[0], read[1]), file = hout_ta)
with open(self.tmp_dir + '/' + cluster_id + ".query.fa", 'w') as hout_qu:
print(">query_%s\n%s" % (cluster_id, self.query_seq), file = hout_qu)
alignment_info = nanomonsv.long_read_validate.ssw_check(
self.tmp_dir + '/' + cluster_id + ".query.fa",
self.tmp_dir + '/' + cluster_id + ".target.fa")
all_rnames = list(set(alignment_info.keys()))
supporting_reads = [rname for rname in all_rnames if \
alignment_info[rname][0] > self.score_ratio_thres * len(self.query_seq) and \
alignment_info[rname][1] < self.start_pos_thres * len(self.query_seq) and \
alignment_info[rname][2] > self.end_pos_thres * len(self.query_seq)]
if "15033196" in self.key:
for a in alignment_info: print(a, alignment_info[a], a in supporting_reads)
print(' ')
"""
# print(cluster_id)
all_rnames = []
supporting_reads = []
align_res = []
for target_read in self.target_seq_list:
all_rnames.append(target_read[0])
tres = edlib.align(self.query_seq,
target_read[1],
mode="HW",
task="locations")
# print(target_read[0])
# print(tres)
if tres["editDistance"] < len(self.query_seq) * 0.25: # and \
# tres["locations"][0][0] < 0.2 * len(self.query_seq) and \
# tres["locations"][0][1] > 0.8 * len(self.query_seq):
supporting_reads.append(target_read[0])
return (len(all_rnames), len(supporting_reads))
bam_hin = pysam.AlignmentFile(bam_file, 'rb')
reference_fasta = pysam.FastaFile(reference)
rname2key = {}
key2contig = {}
with open(input_file, 'r') as hin:
for row in csv.reader(hin, delimiter='\t'):
key = '\t'.join(row[:4])
for read in bam_hin.fetch(row[1], max(int(row[2]) - 100, 0),
int(row[2]) + 100):
if read.is_secondary: continue
if read.qname not in rname2key: rname2key[read.qname] = []
rname2key[read.qname].append(key)
if row[3] == '+':
contig = reference_fasta.fetch(
row[1], max(int(row[2]) - validate_sequence_length - 1, 0),
int(row[2]))
else:
contig = reference_fasta.fetch(
row[1],
int(row[2]) - 1,
int(row[2]) + validate_sequence_length - 1)
contig = reverse_complement(contig)
contig = contig + row[4][:validate_sequence_length]
key2contig[key] = contig
for rname in rname2key:
keys = list(set(rname2key[rname]))
rname2key[rname] = keys
with open(output_file + ".tmp.long_read_seq.unsorted", 'w') as hout:
for read in bam_hin.fetch():
if read.is_secondary or read.is_supplementary: continue
if read.qname in rname2key:
read_seq = read.query_sequence
read_seq = reverse_complement(
read.query_sequence
) if read.is_reverse else read.query_sequence
for key in rname2key[read.qname]:
print("%s\t%s\t%s" % (key, read.qname, read_seq),
file=hout)
bam_hin.close()
with open(output_file + ".tmp.long_read_seq.sorted", 'w') as hout:
subprocess.check_call(
["sort", "-k1,1", output_file + ".tmp.long_read_seq.unsorted"],
stdout=hout)
os.remove(output_file + ".tmp.long_read_seq.unsorted")
# key2count = {}
alignment_counter = Alignment_counter(score_ratio_thres, start_pos_thres,
end_pos_thres)
with open(output_file + ".tmp.long_read_seq.sorted",
'r') as hin, open(output_file, 'w') as hout:
for row in csv.reader(hin, delimiter='\t'):
key = '\t'.join(row[:4])
if key != alignment_counter.key:
acount = alignment_counter.count_alignment()
if acount is not None:
print("%s\t%d\t%d" %
(alignment_counter.key, acount[0], acount[1]),
file=hout)
# key2count[alignment_counter.key] = acount
alignment_counter.initialize(key, key2contig[key])
alignment_counter.add_query_seq(row[4], row[5])
acount = alignment_counter.count_alignment()
if acount is not None:
print("%s\t%d\t%d" % (alignment_counter.key, acount[0], acount[1]),
file=hout)
# key2count[alignment_counter.key] = acount
del alignment_counter
os.remove(output_file + ".tmp.long_read_seq.sorted")
import neural_network as nn
import utils as util
from random import shuffle
import numpy as np
import random
#read in sites
posfile = '/Users/student/Documents/Algorithms/Alg_final_project/data/rap1-lieb-positives.txt'
negfile = '/Users/student/Documents/Algorithms/Alg_final_project/data/yeast-upstream-1k-negative.fa'
testfile = '/Users/student/Documents/Algorithms/Alg_final_project/data/rap1-lieb-test.txt'
poslist = util.read_pos(posfile)
finaltestlist = util.read_pos(testfile)
posreversecomp = []
for i in poslist:
posreversecomp.append(util.reverse_complement(i))
poslist = poslist + posreversecomp
neglist = util.read_fasta(negfile)
negreversecomp = []
for i in neglist:
negreversecomp.append(util.reverse_complement(i))
neglist = neglist + negreversecomp
for i in neglist:
if i in set(poslist):
neglist.remove(i)
#print('negs',neglist[:10])
print('neg', len(neglist))