def rc_regions(gb, choice='whole'):
"""
Reverse and complement given region of sequence.
Args:
gb(Path or str): rotate_seq generated gb file
choice(str): region to be processed, must be in 'LSC', 'IRa', 'SSC',
'IRb', 'whole'.
Return:
new_file(Path): reverse-complemented fasta
"""
# choices = ('LSC', 'IRa', 'SSC', 'IRb', 'whole')
raw = SeqIO.read(gb, 'gb')
data = {}
new_seq = ''
regions = get_regions(gb)
for r in regions:
data[r] = regions[r].extract(raw).seq
if choice != 'whole':
data[choice] = rc(regions[choice].extract(raw.seq))
new_seq = data['LSC']
for i in ['IRa', 'SSC', 'IRb']:
new_seq += data[i]
else:
new_seq = rc(raw.seq)
new_name = '_RC_' + raw.name
new_file = gb.with_suffix('.rc.rc')
with open(new_file, 'w') as _:
_.write(f'>{new_name}\n')
_.write(f'{new_seq}\n')
return new_file
def main():
usage = "usage: %prog [options]"
description = "blah blah blah"
optparser = OptionParser(version="%prog 0.1",description=description,usage=usage,add_help_option=False)
optparser.add_option("-h","--help",action="help",help="Show this help message and exit.")
optparser.add_option("-i","--ifile",dest="ifile",type="string",
help="input files, if you give a pattern for files, please use \" to surround the pattern string")
optparser.add_option("-w","--window",dest="window",type="int",
default=1000,help="window to extract a sample sequence, default:1000")
optparser.add_option("-s","--sample",dest="sample",type="int",
default=36,help="size of a sample sequence,default=36")
optparser.add_option("-f","--frag",dest="frag",type="int",
help="if pair-end calculation is needed, give the fragment size")
optparser.add_option("-o","--ofile",dest="ofile",
help="output file")
(options,args) = optparser.parse_args()
if not options.ifile or not options.window or not options.sample or not options.ofile:
optparser.print_help()
sys.exit(1)
ohd = open(options.ofile,"w")
files = glob(options.ifile)
if not files:
sys.stderr.write("no file found: %s\n" % (options.ifile))
sys.exit(1)
m = 0
for f in files:
sys.stdout.write("%s ... \n" % (f))
sys.stdout.flush()
fahd = open(f,"r")
record = SeqIO.parse(fahd,"fasta")
for i in record:
n = len(i.seq)/1000
for l in range(0,n-1):
s = l*1000+randint(0,999)
if options.frag:
slice = i.seq[s:s+options.frag].tostring().upper()
if slice.find("N") == -1:
m+=1
ohd.write("%s\t%d\n" % (slice[:options.sample],m))
m+=1
ohd.write("%s\t%d\n" % (rc(slice[-1*options.sample:]),m))
#ohd.write("> slice%d_left\n%s\n" % (m,slice[:options.sample]))
#ohd.write("> slice%d_right\n%s\n" % (m,rc(slice[-1*options.sample:])))
else:
slice = i.seq[s:s+options.sample].tostring().upper()
if slice.find("N") == -1:
m+=1
ohd.write("%s\t%d\n" % (slice,m))
#ohd.write("> slice%d\n%s\n" % (m,slice))
fahd.close()
ohd.close()
def cseguid(seq):
'''Returns the cSEGUID for the sequence. The cSEGUID is the url safe SEGUID checksum
calculated for the lexicographically minimal string rotation of a DNA sequence.
Only defined for circular sequences.
'''
from Bio.Seq import reverse_complement as rc
return pretty_string(
seguid(
min(SmallestRotation(str(seq).upper()),
SmallestRotation(str(rc(seq)).upper()))))
def _rc(record):
if isinstance(record, str):
return rc(record)
elif isinstance(record, Seq):
return record.reverse_complement()
elif isinstance(record, SeqRecord):
return SeqRecord(record.seq.reverse_complement(),
id=record.id,
name=record.name,
description=record.description)
else:
raise ValueError(
'record must be one of str, Bio.Seq, or Bio.SeqRecord')
def primer_dict(self):
if self._primer_dict is None:
from Bio.Seq import reverse_complement as rc
self._primer_dict = {'F1': ['CTCAATAAAGCTTGCCTTGAGTGC', rc('ACTGTATCATCTGCTCCTGTRTCT')],
'F2': ['AAATTGCAGGGCYCCTAG', rc('CTRTTAGCTGCCCCATCTACATAG')],
'F3B': ['CACACTAATGATGTAARACARTTAACAG', rc('GGGATGTGTACTTCTGAACTTAYTYTTGG')],
'F4': ['CGGGTTTATTWCAGRGACAGCAGA', rc('GGGGTTAAYTTTACACATGGYTTTA')],
'F5a': ['GGCATYTCCTATGGCAGGAAGAAG', rc('GTGGTGCARATGAGTTTTCCAGAGCA')],
'F6': ['GGGTTCTTRGGARCAGCAGGAAG', rc('ATTGAGGCTTAAGCAGTGGGTTC')],}
return self._primer_dict
def _rc(record):
if isinstance(record, str):
return rc(record)
elif isinstance(record, Seq):
return record.reverse_complement()
elif isinstance(record, SeqRecord):
return SeqRecord(
record.seq.reverse_complement(),
id=record.id,
name=record.name,
description=record.description
)
else:
raise ValueError(
'record must be one of str, Bio.Seq, or Bio.SeqRecord'
)
def reads_to_seqrecord(reads):
'''Build a FASTQ record out of BAM reads
Note: copied from Bio.SeqIO.QualityIO.py
'''
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
# Precompute conversion table
SANGER_SCORE_OFFSET = ord("!")
q_mapping = dict()
for letter in xrange(0, 255):
q_mapping[chr(letter)] = letter - SANGER_SCORE_OFFSET
seqs = []
for read in reads:
# Get the sequence first
descr = read.qname
id = read.qname
name = id
from Bio.Alphabet import IUPAC
from Bio.Alphabet import IUPAC
from Bio.Seq import reverse_complement as rc
if not read.is_reverse:
record = SeqRecord(Seq(read.seq, IUPAC.ambiguous_dna),
id=id, name=name, description=descr)
if read.is_reverse:
record = SeqRecord(Seq(rc(read.seq), IUPAC.ambiguous_dna),
id=id, name=name, description=descr)
# Get the qualities second
qualities = [q_mapping[letter] for letter in read.qual]
if qualities and (min(qualities) < 0 or max(qualities) > 93):
raise ValueError("Invalid character in quality string")
dict.__setitem__(record._per_letter_annotations,
"phred_quality", qualities)
seqs.append(record)
return (seqs)
fragments_exons = {g: frs for (g, frs) in fragments_genes.iteritems()
if g not in ('tat', 'rev')}
fragments_exons['tat1'] = fragments_genes['tat'][0]
fragments_exons['tat2'] = fragments_genes['tat'][1]
fragments_exons['rev1'] = fragments_genes['rev'][0]
fragments_exons['rev2'] = fragments_genes['rev'][1]
fragments_RNA_structures = {'RRE': ['F5', 'F6'],
"LTR5'": ['F1'],
"LTR3'": ['F6']}
fragments_other = {'env peptide': ['F4', 'F5'],
'psi': ['F1']}
# Note: the reverse primers get reverse complemented (so everything is positive sense)
primers_inner = {'F1': ['AAGTAGTGTGTGCCCGTCTGT', rc('TGCCAAAGAGTGATYTGAGGG')],
'F2': ['GGGCTGTTGGARATGTGG', rc('ACAAACTCCCAYTCAGGAATCCA')],
'F3a': ['GAAAGCATAGTRATATGGGGAAA', rc('CACCTGCCATCTGTTTTCCATA')],
'F3b': ['GAAAGCATAGTRATATGGGGAAA', rc('CACCTGCCATCTGTTTTCCATA')],
'F3B': ['GAAAGCATAGTRATATGGGGAAA', rc('CACCTGCCATCTGTTTTCCATA')],
'F4': ['TGGAAAGGTGAAGGGGCAG', rc('GTACACAGGCATGTGTRGCCCA')],
'F5a': ['TAAGAGAAAGAGCAGAAGACAGTGG', rc('CCAAATYCCYAGGAGCTGTTGATC')],
'F5b': ['TCTATTATGGRGTACCTGTRTGG', rc('CCAAATYCCYAGGAGCTGTTG')],
'F6': ['CAGGAAGCACTATGGGCGC', rc('CCAGAGAGCTCCCAGG')],
}
primers_outer = {'F1': ['CTCAATAAAGCTTGCCTTGAGTGC', rc('ACTGTATCATCTGCTCCTGTRTCT')],
'F2': ['AAATTGCAGGGCYCCTAG', rc('CTRTTAGCTGCCCCATCTACATAG')],
'F3a': ['CACACTAATGATGTAARACARTTAACAG', rc('TTCCATGTTYTAATCCTCATCCTGTCTAC')],
# NOTE: F3b and F3B are actually the same, but I forgot about the last G for
# the biggest part of the dataset. It's not a huge problem because that G is
# Edges of RNA structures
RRE_edges = ['AGGAGCTATGTTCCTTGGGT', 'ACCTAAGGGATACACAGCTCCT']
LTR5 = [None, 'CTCTAGCA']
LTR3 = ['TGGANGGGNTANTTNNNTC', None]
RNA_structure_edges = {'RRE': RRE_edges, "LTR5'": LTR5, "LTR3'": LTR3}
# Edges of other regions
env_peptide_edges = ['ATGAGAGTGAAGGAGAA', 'GCTCCTTGGGATGTTGATGATCTGTAGTGCT']
psi_element = ['CTCGGCTTGCT', 'AGCGGAGGCTAG']
# V1, V3, V4, and V5 actually start INSIDE these primers
V1_edges = [
'AANCCATGTGTAAAANTAACNCCACTNTGTGTNANTTTANAN',
'TGCTCTTTCAATNTCANCNCANNNNTAANA'
]
V3_edges = ['ACAATGYACACATGGAATTARGCCA', rc('AGAAAAATTCYCCTCYACAATTAAA')]
V4_edges = [
'TTGTAANGCACANTTTTAATTGTGGAGGGGAATTTTTCTAC',
'AGAATAANACAAATTNTAAACANGTGGCAGNAAGTAGGA'
]
V5_edges = [
'ATCAAATATTACAGGGNTNNTAACAAGAGATGGNGGN',
'GNAGGAGGANATATGANGGANAATTGGAGAAGT'
]
# V2 is particular: from the left it starts right there, from the right it ends
# INSIDE this primer
V2_edges = [
'TGCTCTTTCAATNTCANCNCANNNNTAANA',
'AANACCTCANTCATTACACANGCNTGTCCAAANNTATCCTTTGANCCAATTCC'
]
gp120_noVloops_edges = [
fragments_exons['tat2'] = fragments_genes['tat'][1]
fragments_exons['rev1'] = fragments_genes['rev'][0]
fragments_exons['rev2'] = fragments_genes['rev'][1]
fragments_RNA_structures = {
'RRE': ['F5', 'F6'],
"LTR5'": ['F1'],
"LTR3'": ['F6']
}
fragments_other = {'env peptide': ['F4', 'F5'], 'psi': ['F1']}
# Note: the reverse primers get reverse complemented (so everything is positive sense)
primers_inner = {
'F1': ['AAGTAGTGTGTGCCCGTCTGT',
rc('TGCCAAAGAGTGATYTGAGGG')],
'F2': ['GGGCTGTTGGARATGTGG',
rc('ACAAACTCCCAYTCAGGAATCCA')],
'F3a': ['GAAAGCATAGTRATATGGGGAAA',
rc('CACCTGCCATCTGTTTTCCATA')],
'F3b': ['GAAAGCATAGTRATATGGGGAAA',
rc('CACCTGCCATCTGTTTTCCATA')],
'F3B': ['GAAAGCATAGTRATATGGGGAAA',
rc('CACCTGCCATCTGTTTTCCATA')],
'F4': ['TGGAAAGGTGAAGGGGCAG',
rc('GTACACAGGCATGTGTRGCCCA')],
'F5a': ['TAAGAGAAAGAGCAGAAGACAGTGG',
rc('CCAAATYCCYAGGAGCTGTTGATC')],
'F5b': ['TCTATTATGGRGTACCTGTRTGG',
rc('CCAAATYCCYAGGAGCTGTTG')],
'F6': ['CAGGAAGCACTATGGGCGC',
RRE_edges = ['AGGAGCTATGTTCCTTGGGT', 'ACCTAAGGGATACACAGCTCCT']
LTR5 = [None, 'CTCTAGCA']
LTR3 = ['TGGANGGGNTANTTNNNTC', None]
RNA_structure_edges = {'RRE': RRE_edges,
"LTR5'": LTR5,
"LTR3'": LTR3}
# Edges of other regions
env_peptide_edges = ['ATGAGAGTGAAGGAGAA', 'GCTCCTTGGGATGTTGATGATCTGTAGTGCT']
psi_element = ['CTCGGCTTGCT', 'AGCGGAGGCTAG']
# V1, V3, V4, and V5 actually start INSIDE these primers
V1_edges = ['AANCCATGTGTAAAANTAACNCCACTNTGTGTNANTTTANAN',
'TGCTCTTTCAATNTCANCNCANNNNTAANA']
V3_edges = ['ACAATGYACACATGGAATTARGCCA', rc('AGAAAAATTCYCCTCYACAATTAAA')]
V4_edges = ['TTGTAANGCACANTTTTAATTGTGGAGGGGAATTTTTCTAC',
'AGAATAANACAAATTNTAAACANGTGGCAGNAAGTAGGA']
V5_edges = ['ATCAAATATTACAGGGNTNNTAACAAGAGATGGNGGN', 'GNAGGAGGANATATGANGGANAATTGGAGAAGT']
# V2 is particular: from the left it starts right there, from the right it ends
# INSIDE this primer
V2_edges = ['TGCTCTTTCAATNTCANCNCANNNNTAANA',
'AANACCTCANTCATTACACANGCNTGTCCAAANNTATCCTTTGANCCAATTCC']
gp120_noVloops_edges = ['NNAGAANANTTGTGGGTCACAGTCTATTATGGGGTACCT',
'AAAGCCTAAAGCCATGTGTAAAATTAACCCCACTCTGTGTTAGTTTAAAG',
'AANACCTCANTCATTACACANGCNTGTCCAAANNTATCCTTTGANCCAATTCC',
'ACAGTACAATGTACACATGGAATTANNCCA',
'TTTAATTGTGGAGGGGAATTTTTCT',
'GGGGAATTTTTCTAC',
'GAATAAAACAAATTNTAAACANGTGGCAGAAAGTAGGAAAAGCA',
'ATTACAGGGNTNNTATTAACAAGAGATGGTGGT',
def cseguid(seq):
'''Returns the cSEGUID for the sequence. The cSEGUID is the url safe SEGUID checksum
calculated for the lexicographically minimal string rotation of a DNA sequence.
Only defined for circular sequences.
'''
from Bio.Seq import reverse_complement as rc
return pretty_string( seguid( min( SmallestRotation(str(seq).upper()), SmallestRotation(str(rc(seq)).upper()))))
def copy_features(source_sr, target_sr, limit = 10):
'''This function tries to copy all features in source_seq and copy
them to target_seq. Source_sr and target_sr are objects with
a features property, such as Dseqrecord or Biopython SeqRecord.
Parameters
----------
source_seq : SeqRecord or Dseqrecord
The sequence to copy features from
target_seq : SeqRecord or Dseqrecord
The sequence to copy features to
Returns
-------
bool : True
This function acts on target_seq in place.
No data is returned.
'''
import re
from Bio.Seq import reverse_complement as rc
target_length = len(target_sr)
target_string = str(target_sr.seq).upper()
try:
circular = bool(target_sr.circular)
except AttributeError:
circular=False
newfeatures=[]
trgt_string = target_string
trgt_string_rc = rc(trgt_string)
for feature in [f for f in source_sr.features if len(f)>limit]:
fsr = feature.extract(source_sr).upper()
featurelength = 0# len(fsr)
if circular:
trgt_string = target_string+target_string[:featurelength]
trgt_string_rc = rc(trgt_string)
positions = (
[(m.start(), m.end(), 1,) for m in re.finditer(str(fsr.seq),trgt_string)]
+
[(len(trgt_string_rc)-m.end(),len(trgt_string_rc)-m.start(),-1,)
for m in re.finditer(str(fsr.seq),trgt_string_rc)])
for begin, end, strand in positions:
if circular and begin<target_length<end:
end = end-len(
target_sr)
sf1 = SeqFeature(FeatureLocation(begin, trgt_length),
type=feature.type,
location_operator=feature.location_operator,
strand=strand,
id=feature.id,
qualifiers=feature.qualifiers,
sub_features=None,)
sf2 = SeqFeature(FeatureLocation(0, end),
type=feature.type,
location_operator=feature.location_operator,
strand=strand,
id=feature.id,
qualifiers=feature.qualifiers,
sub_features=None,)
nf = SeqFeature(FeatureLocation(begin, end),
type=feature.type,
location_operator="join",
strand=strand,
id=feature.id,
qualifiers=feature.qualifiers,
sub_features=[sf1,sf2],)
else:
nf = SeqFeature(FeatureLocation(begin,end),
type=feature.type,
location_operator=feature.location_operator,
strand=strand,
id=feature.id,
qualifiers=feature.qualifiers,
sub_features=None)
newfeatures.append(nf)
target_sr.features.extend(newfeatures)
return True
def filter_tendem(gen):
for code in gen:
if max_rep(code) > 2:
continue
else:
yield code
if __name__ == "__main__":
codes = barcode_gen(8)
codes = filter_tendem(codes)
acc = KeepDist(3)
codes = acc.filter(codes)
codes = islice(codes, 96)
#print("index\tbarcode\tlinker_F\tlinker_R")
for i, code in enumerate(codes):
a1 = "GTCGGA" + code + "G"
a2 = rc(a1)[::-1]
a1 = "TA" + a1
a2 = a2 + "GATC"
a2 = a2[::-1]
#items = [str(i), code, a1, a2]
#print("\t".join(items))
if i < 10:
idx = '0' + str(i)
else:
idx = str(i)
print(f"MseI-linker-{idx}-F\t{a1}")
print(f"MseI-linker-{idx}-R\t{a2}")
variants_in_gene = {}
pb = ProgressBar(maxval=UnknownLength)
for line in pb(open(variants)):
chrom, pos, _, variant = line.split()
pos = int(pos)
for gene_chrom, low, high in gene_coords:
if chrom == gene_chrom and low <= pos <= high:
variant = variant.split('|')
mel, sim = variant
#assert chroms[chrom][pos] == 'N'
chroms[chrom][pos] = ambigs[tuple(sorted(variant))]
mel_copy[chrom][pos] = mel
sim_copy[chrom][pos] = sim
assert mel_copy[chrom][pos] != sim_copy[chrom][pos]
if gene_strand == '-':
variant = (rc(variant[0]),
rc(variant[1]))
variants_in_gene[pos] = variant
break
# Build the hunchback transcript
gene_str = []
mel_str = []
sim_str = []
gene_bps = []
for chrom, start, stop in gene_coords:
gene_str.append(str(chroms[chrom][start:stop]))
mel_str.append(str(mel_copy[chrom][start:stop]))
sim_str.append(str(sim_copy[chrom][start:stop]))
gene_bps.extend(range(start, stop))