def main():
parser = get_parser()
args = validate_args(parser)
seq_records = seqrecords.read_fasta(args.fasta)
if args.word_size:
p = word_pattern.create(seq_records.seq_list, args.word_size)
else:
p = word_pattern.read(args.word_pattern)
if args.reduce_alphabet:
p = p.reduce_alphabet(seqcontent.get_reduced_alphabet(args.molecule))
if args.merge_revcomp:
p = p.merge_revcomp()
freqs = word_vector.Freqs(seq_records.length_list, p)
dist = word_distance.Distance(freqs, args.distance)
matrix = distmatrix.create(seq_records.id_list, dist)
if args.out:
oh = open(args.out, 'w')
matrix.write_to_file(oh, args.outfmt)
oh.close()
else:
matrix.display(args.outfmt)
def main():
parser = get_parser()
args = validate_args(parser)
seq_records = seqrecords.read_fasta(args.fasta)
dist = wmetric.Distance(seq_records, args.matrix)
matrix = distmatrix.create(seq_records.id_list, dist)
if args.out:
oh = open(args.out, 'w')
matrix.write_to_file(oh, args.outfmt)
oh.close()
else:
matrix.display(args.outfmt)
def main():
parser = get_parser()
args = validate_args(parser)
seq_records = seqrecords.read_fasta(args.fasta)
vector = bbc.create_vectors(seq_records, args.k, alphabet=args.alphabet)
dist = bbc.Distance(vector)
matrix = distmatrix.create(seq_records.id_list, dist)
if args.out:
oh = open(args.out, 'w')
matrix.write_to_file(oh, args.outfmt)
oh.close()
else:
matrix.display(args.outfmt)
def main():
parser = get_parser()
args = validate_args(parser)
seq_records = seqrecords.read_fasta(args.fasta)
if args.vector == '2DSV':
vector = graphdna.create_2DSGraphVectors(seq_records)
elif args.vector == '2DNV':
vector = graphdna.create_2DNGraphVectors(seq_records)
else:
vector = graphdna.create_2DMGraphVectors(seq_records, args.ndim)
dist = graphdna.Distance(vector)
matrix = distmatrix.create(seq_records.id_list, dist)
if args.out:
oh = open(args.out, 'w')
matrix.write_to_file(oh, args.outfmt)
oh.close()
else:
matrix.display(args.outfmt)
def main():
parser = get_parser()
args = validate_args(parser)
seq_records = seqrecords.read_fasta(args.fasta)
if args.word_size:
p = word_pattern.create(seq_records.seq_list, args.word_size)
else:
p = word_pattern.read(args.word_pattern)
bools = word_vector.Bools(seq_records.length_list, p)
dist = word_bool_distance.Distance(bools, args.distance)
matrix = distmatrix.create(seq_records.id_list, dist)
if args.out:
oh = open(args.out, 'w')
matrix.write_to_file(oh, args.outfmt)
oh.close()
else:
matrix.display(args.outfmt)
def main():
parser = get_parser()
args = validate_args(parser)
seq_records = seqrecords.read_fasta(args.fasta)
if args.word_size:
p = word_pattern.create(seq_records.seq_list, args.word_size)
else:
p = word_pattern.read(args.word_pattern)
veccls = {'counts': word_vector.Counts, 'freqs': word_vector.Freqs}
vecclsw = {
'counts': word_vector.CountsWeight,
'freqs': word_vector.FreqsWeight
}
if args.vector == 'counts' or args.vector == 'freqs':
if args.char_weights is None:
vec = veccls[args.vector](seq_records.length_list, p)
else:
weightmodel = word_vector.WeightModel(
char_weights=args.char_weights)
vec = vecclsw[args.vector](seq_records.length_list, p, weightmodel)
else:
if args.alphabet_size:
freqmodel = word_vector.EqualFreqs(
alphabet_size=args.alphabet_size)
else:
freqmodel = word_vector.EquilibriumFreqs(args.char_freqs)
vec = word_vector.FreqsStd(seq_records.length_list, p, freqmodel)
dist = word_distance.Distance(vec, args.distance)
matrix = distmatrix.create(seq_records.id_list, dist)
if args.out:
oh = open(args.out, 'w')
matrix.write_to_file(oh, args.outfmt)
oh.close()
else:
matrix.display(args.outfmt)
def main():
parser = get_parser()
args = validate_args(parser)
seq_records = seqrecords.read_fasta(args.fasta)
patterns = []
for i in range(args.min_word_size, args.max_word_size + 1):
p = word_pattern.create(seq_records.seq_list, i)
patterns.append(p)
vecs = []
if args.char_weights is not None:
weightmodel = word_vector.WeightModel(char_weights=args.char_weights)
vecklas = {
'counts': word_vector.CountsWeight,
'freqs': word_vector.FreqsWeight
}[args.vector]
kwargs = {
'seq_lengths': seq_records.length_list,
'weightmodel': weightmodel
}
else:
vecklas = {
'counts': word_vector.Counts,
'freqs': word_vector.Freqs
}[args.vector]
kwargs = {'seq_lengths': seq_records.length_list}
for p in patterns:
v = vecklas(patterns=p, **kwargs)
vecs.append(v)
dist = word_d2.Distance(vecs)
matrix = distmatrix.create(seq_records.id_list, dist)
if args.out:
oh = open(args.out, 'w')
matrix.write_to_file(oh, args.outfmt)
oh.close()
else:
matrix.display(args.outfmt)
def main():
parser = get_parser()
args = validate_args(parser)
seq_records = seqrecords.read_fasta(args.fasta)
if args.word_size:
p = word_pattern.create(seq_records.seq_list, args.word_size, True)
else:
p = args.word_pattern
vector = word_rtd.create_vector(seq_records.count, p)
dist = word_rtd.Distance(vector, args.distance)
matrix = distmatrix.create(seq_records.id_list, dist)
if args.out:
oh = open(args.out, 'w')
matrix.write_to_file(oh, args.outfmt)
oh.close()
else:
matrix.display(args.outfmt)
def main():
parser = get_parser()
args = validate_args(parser)
if args.teiresias:
args.fasta.close()
p = word_pattern.run_teiresias(args.fasta.name,
w=args.word_size,
l=args.l,
k=args.k,
output_filename=args.out)
else:
seq_records = seqrecords.read_fasta(args.fasta)
args.fasta.close()
p = word_pattern.create(seq_records.seq_list, args.word_size,
args.word_position)
if args.out:
oh = open(args.out, 'w')
oh.write(p.format())
oh.close()
else:
print(p.format())
def main():
parser = get_parser()
args = validate_args(parser)
seq_records = seqrecords.read_fasta(args.fasta)
if args.word_patterns:
l = args.word_patterns
else:
l = []
for i in range(args.word_size, args.word_size - 3, -1):
p = word_pattern.create(seq_records.seq_list, i)
l.append(p)
compos = word_vector.Composition(seq_records.length_list, *l)
dist = word_distance.Distance(compos, 'angle_cos_diss')
matrix = distmatrix.create(seq_records.id_list, dist)
if args.out:
oh = open(args.out, 'w')
matrix.write_to_file(oh, args.outfmt)
oh.close()
else:
matrix.display(args.outfmt)
from alfpy import word_d2
infile = ""
outfile = ""
method = ""
if len(sys.argv) > 4:
sys.stderr.write("Too many arguments\n")
exit(1)
else:
method = sys.argv[1]
infile = sys.argv[2]
outfile = sys.argv[3]
input_file = open(infile, 'r')
seq_records = seqrecords.read_fasta(input_file)
input_file.close()
#choose one method to compute distance matrix
if method == "bbc":
alphabet = seqcontent.get_alphabet('protein')
vector = bbc.create_vectors(seq_records, 10, alphabet)
dist = bbc.Distance(vector)
matrix = distmatrix.create(seq_records.id_list, dist)
matrix.display()
elif method == "ncd":
dist = ncd.Distance(seq_records)
matrix = distmatrix.create(seq_records.id_list, dist)
matrix.display()
def create_from_fasta(handle, word_size=1, wordpos=False):
"""Create word patterns (Pattern object) from a FASTA file"""
seq_records = seqrecords.read_fasta(handle)
return create(seq_records.seq_list, word_size=word_size, wordpos=wordpos)
