def calc_group_unique_allele_window(args):
"""Count the number of and relative rate of uniquely held alleles
spatially along chromosomes (i.e. Lineage-specific rates)"""
data = {}
mvf = MultiVariantFile(args.mvf, 'read')
if mvf.flavor != 'codon':
raise RuntimeError(
"\n=====================\nERROR: MVF is not codon flavor!")
annotations = {}
coordinates = {}
labels = mvf.get_sample_labels()[:]
ncol = len(labels)
current_contig = None
current_position = 0
counts = Counter()
totals = Counter()
args.start_contig = (args.start_contig
if args.start_contig is not None else 0)
args.end_contig = (args.end_contig
if args.end_contig is not None else 100000000000)
if args.output_align is True:
outputalign = []
if args.gff is not None:
annotations, coordinates = (parse_gff_analysis(args.gff))
if args.allele_groups is not None:
args.allele_groups = procarg_allelegroups(args.allele_groups, mvf)
if args.species_groups is None:
args.species_groups = args.allele_groups
else:
args.species_groups = procarg_speciesgroups(args.species_groups, mvf)
fieldtags = [
'likelihood', 'bgdnds0', 'bgdnds1', 'bgdnds2a', 'bgdnds2b', 'fgdnds0',
'fgdnds1', 'fgdnds2a', 'fgdnds2b', 'dndstree', 'errorstate'
]
if args.branch_lrt is not None:
with open(args.branch_lrt, 'w') as branchlrt:
genealign = []
branchlrt.write(
"\t".join(['contig', 'ntaxa', 'alignlength', 'lrtscore'] +
["null.{}".format(x) for x in fieldtags] +
["test.{}".format(x)
for x in fieldtags] + ['tree']) + "\n")
groups = args.allele_groups.values()
if args.species_groups is not None:
speciesgroups = args.species_groups.values()
allsets = set([])
for group in groups:
allsets.update(group)
allsets = list(sorted(allsets))
speciesnames = args.species_groups.keys()
speciesrev = {}
if args.species_groups is not None:
for species in args.species_groups:
speciesrev.update([(x, species)
for x in args.species_groups[species]])
if args.mincoverage is not None:
if args.mincoverage < len(groups) * 2:
raise RuntimeError("""
Error: GroupUniqueAlleleWindow:
--mincoverage cannot be lower than the twice the number
of specified groups in --allele-groups
""")
genealign = []
for contig, pos, allelesets in mvf:
if not current_contig:
current_contig = contig[:]
if contig != current_contig or (args.windowsize > 0 and pos >
current_position + args.windowsize):
xkey = (
current_contig,
current_position,
)
data[xkey] = counts.copy()
data[xkey].update([
('contig', (mvf.get_contig_labels(ids=current_contig)
if args.use_labels is True else current_contig)),
('position', current_position),
('nonsynyonymous_changes',
counts.get('nonsynonymous_changes', 0) or 0),
('synyonymous_changes', counts.get('synonymous_changes', 0)
or 0)
])
data[xkey].update([
('ns_ratio',
(float(data[xkey].get('nonsynonymous_changes', 0)) /
(data[xkey].get('synonymous_changes', 1.0)))),
('annotation', annotations.get(data[xkey]['contig'], '.')),
('coordinates', coordinates.get(data[xkey]['contig'], '.'))
])
if genealign:
if (args.end_contig >= int(current_contig)) and (
args.start_contig <= int(current_contig)):
(pamlnull, pamltest, tree) = paml_branchsite(
genealign,
labels[:],
species=speciesnames,
speciesrev=speciesrev,
codemlpath=args.codeml_path,
raxmlpath=args.raxml_path,
pamltmp=args.paml_tmp,
target=args.target,
targetspec=args.num_target_species,
allsampletrees=args.all_sample_trees,
outgroup=args.outgroup)
lrtscore = -1
if (pamlnull.get('likelihood', -1) != -1
and pamltest.get('likelihood', -1) != -1):
lrtscore = 2 * (pamltest['likelihood'] -
pamlnull['likelihood'])
with open(args.branch_lrt, 'a') as branchlrt:
branchlrt.write("\t".join([
str(x) for x in [
data[xkey]['contig'],
len(genealign),
len(genealign[0]) * 3, lrtscore
] + [pamlnull.get(y, -1) for y in fieldtags] +
[pamltest.get(y, -1)
for y in fieldtags] + [str(tree).rstrip()]
]) + "\n")
genealign = None
totals.add('genes_total')
if counts.get('total_codons', 0) > 0:
totals.add('genes_tested')
if counts.get('total_nsyn_codons', 0) > 0:
totals.add('genes_with_nsyn')
if contig != current_contig:
current_contig = contig[:]
current_position = 0
elif args.windowsize > 0:
current_position += args.windowsize
counts = Counter()
proteins = allelesets[0]
codons = allelesets[1:4]
if len(proteins) == 1 and all(len(x) == 1 for x in codons):
if proteins == '*' or ''.join(codons) in MLIB.stop_codons:
continue
counts.add('total_codons')
totals.add('total_codons')
if args.output_align is True:
if not outputalign:
outputalign = [[''.join(codons)]
for x in range(mvf.metadata['ncol'])]
else:
for ialign, xalign in enumerate(outputalign):
xalign.append(''.join(codons))
if args.branch_lrt is not None:
if not genealign:
genealign = [[''.join(codons)] for x in range(ncol)]
else:
for ialign in range(len(genealign)):
genealign[ialign].append(''.join(codons))
continue
if len(proteins) > 1:
if allelesets[0][1] == '+':
continue
proteins = mvf.decode(proteins)
if args.mincoverage is not None:
if sum([int(x not in 'X-')
for x in proteins]) < (args.mincoverage):
continue
species_groups = [[proteins[i] for i in x if proteins[i] not in '-X']
for x in speciesgroups]
if any(len(x) == 0 for x in species_groups):
continue
xcodons = [mvf.decode(x) for x in codons]
codons = [''.join(x) for x in zip(*xcodons)]
if any(codons[x] in MLIB.stop_codons for x in allsets):
continue
if any(
any(x != species_groups[0][0] for x in y)
for y in species_groups):
totals.add('total_nsyn_codons')
counts.add('total_nsyn_codons')
totals.add('total_codons')
totals.add('tested_codons')
counts.add('total_codons')
totals.add('variable_codons',
val=int(
sum([int(len(set(x) - set('X-')) > 1)
for x in xcodons]) > 0))
if args.output_align is not None:
if not outputalign:
outputalign = [[x] for x in codons]
else:
for ialign in range(len(outputalign)):
outputalign[ialign].append(codons[ialign])
if args.branch_lrt is not None:
if not genealign:
genealign = [[x] for x in codons]
else:
for ialign in range(len(codons)):
genealign[ialign].append(codons[ialign])
nonsyn_change = False
synon_change = False
codon_groups = [
set([
codons[i] for i in x
if '-' not in codons[i] and 'X' not in codons[i]
]) for x in groups
]
protein_groups = None
for i in range(len(codon_groups)):
if any(base in codon for base in 'RYWKMS'
for codon in codon_groups[i]):
codon_groups[i] = hapgroup(codon_groups[i])
if all(
grp1.isdisjoint(grp0)
for grp0, grp1 in combinations(codon_groups, 2)):
protein_groups = [
set([
MLIB.codon_tables['full'][''.join(x)]
for x in codon_groups[i]
]) for i in range(len(codon_groups))
]
if all(
grp1.isdisjoint(grp0)
for grp0, grp1 in combinations(protein_groups, 2)):
nonsyn_change = True
elif all(grp1 == grp0
for grp0, grp1 in combinations(protein_groups, 2)):
synon_change = True
if nonsyn_change:
if args.verbose is True:
print('NON', contig, pos, allelesets,
codon_groups, protein_groups, groups,
mvf.get_contig_labels(ids=contig))
counts.add('nonsynonymous_changes')
totals.add('nonsynonymous_changes')
elif synon_change:
if args.verbose is True:
print('SYN', contig, pos, allelesets,
codon_groups, protein_groups, groups,
mvf.get_contig_labels(ids=contig))
counts.add('synonymous_changes')
totals.add('synonymous_changes')
args.totals = totals
# WRITE OUTPUT
headers = [
"contig", "position", "nonsynonymous_changes", "synonymous_changes",
"ns_ratio", "nonsynonymous_total", "synonymous_total", "pvalue",
"total_codons", "annotation", "coordinates"
]
if args.windowsize == -1:
headers.remove('position')
if args.chi_test is None:
headers.remove('pvalue')
outfile = OutputFile(path=args.out, headers=headers)
sorted_entries = sorted(
[(data[k]['ns_ratio'], k)
for k in data if data[k].get('nonsynonymous_changes', 0) > 0],
reverse=True)
for _, k in sorted_entries:
outfile.write_entry(data[k])
with open(args.out + '.total', 'w') as totalfile:
for entry in args.totals.iter_sorted():
totalfile.write(entry)
if args.output_align is not None:
with open(args.output_align, 'w') as alignfile:
alignfile.write("\n".join([
">{}\n{}".format(mvf.metadata['labels'][i],
''.join(outputalign[i]))
for i in range(len(outputalign))
]))
return ''
def calc_all_character_count_per_sample(args):
"""Count the number of and relative rate of certain bases
spatially along chromosomes
"""
args.qprint("Running CalcAllCharacterCountPerSample")
mvf = MultiVariantFile(args.mvf, 'read')
current_contig = None
current_position = 0
data_in_buffer = False
# Set up sample indices
sample_labels = mvf.get_sample_ids()
if args.sample_indices is not None:
sample_indices = [int(x) for x in args.sample_indices[0].split(",")]
elif args.sample_labels is not None:
sample_indices = mvf.get_sample_indices(
ids=args.sample_labels[0].split(","))
else:
sample_indices = mvf.get_sample_indices()
# Set up contig ids
if args.contig_ids is not None:
contig_ids = args.contig_ids[0].split(",")
elif args.contig_labels is not None:
contig_ids = mvf.get_contig_ids(
labels=args.contig_labels[0].split(","))
else:
contig_ids = None
data = dict((i, {}) for i in sample_indices)
data_characters = [{} for i in sample_indices]
for contig, pos, allelesets in mvf.iterentries(decode=False,
contig_ids=contig_ids):
# Check Minimum Site Coverage
if check_mincoverage(args.mincoverage, allelesets[0]) is False:
continue
if current_contig is None:
current_contig = contig[:]
if args.windowsize > 0:
while pos > current_position + args.windowsize - 1:
current_position += args.windowsize
# Check if windows are specified.
if not same_window((current_contig, current_position),
(contig, pos), args.windowsize):
args.qprint("Processing contig {}".format(current_contig))
for i in sample_indices:
data[i][(current_contig, current_position)] = {
'contig': current_contig,
'position': current_position
}
data[i][(current_contig,
current_position)].update(data_characters[i])
if contig != current_contig:
current_contig = contig[:]
current_position = 0
else:
current_position += (0 if args.windowsize == -1 else
args.windowsize)
data_characters = [{} for i in sample_indices]
data_in_buffer = False
alleles = allelesets[0]
if len(alleles) == 1:
for i in sample_indices:
data_characters[i][alleles[0]] = (
data_characters[i].get(alleles[0], 0) + 1)
else:
alleles = mvf.decode(alleles)
for i in sample_indices:
data_characters[i][alleles[i]] = (
data_characters[i].get(alleles[i], 0) + 1)
data_in_buffer = True
if data_in_buffer:
for i in sample_indices:
data[i][(current_contig, current_position)] = {
'contig': current_contig,
'position': current_position
}
data[i][(current_contig,
current_position)].update(data_characters[i])
# WRITE OUTPUT
all_chars = set([])
for sampleid in data:
for window in data[sampleid]:
all_chars.update([
x for x in data[sampleid][window]
if x not in ('contig', 'position')
])
headers = ['contig', 'position']
headers.extend(list(sorted(all_chars)))
outfile = OutputFile(path=args.out, headers=headers)
for sampleid in sample_indices:
outfile.write("#{}\n".format(sample_labels[sampleid]))
sorted_entries = [(data[sampleid][k]['contig'],
data[sampleid][k]['position'], k)
for k in data[sampleid]]
for _, _, k in sorted_entries:
outfile.write_entry(data[sampleid][k], defaultvalue='0')
return ''
def calc_pairwise_distances(args):
"""Count the pairwise nucleotide distance between
combinations of samples in a window
"""
args.qprint("Running CalcPairwiseDistances")
mvf = MultiVariantFile(args.mvf, 'read')
args.qprint("Input MVF: Read")
data = {}
data_order = []
if args.sample_indices is not None:
sample_indices = [int(x) for x in args.sample_indices[0].split(",")]
elif args.sample_labels is not None:
sample_indices = mvf.get_sample_indices(
ids=args.sample_labels[0].split(","))
else:
sample_indices = mvf.get_sample_indices()
sample_labels = mvf.get_sample_ids(indices=sample_indices)
args.qprint("Calculating for sample columns: {}".format(
list(sample_indices)))
current_contig = None
current_position = 0
data_in_buffer = False
sample_pairs = [tuple(x) for x in combinations(sample_indices, 2)]
base_matches = dict((x, {}) for x in sample_pairs)
all_match = {}
if mvf.flavor == 'dna':
allele_frames = (0, )
args.data_type = 'dna'
elif mvf.flavor == 'prot':
allele_frames = (0, )
args.data_type = 'dna'
elif mvf.flavor == 'codon':
if args.data_type == 'prot':
allele_frames = (0, )
else:
allele_frames = (1, 2, 3)
args.data_type = 'dna'
args.qprint("MVF flavor is: {}".format(mvf.flavor))
args.qprint("Data type is: {}".format(args.data_type))
args.qprint("Ambiguous mode: {}".format(args.ambig))
args.qprint("Processing MVF Records")
pwdistance_function = get_pairwise_function(args.data_type, args.ambig)
if args.emit_counts:
outfile_emitcounts = open(args.out + ".pairwisecounts", 'w')
for contig, pos, allelesets in mvf.iterentries(decode=None):
# Check Minimum Site Coverage
if check_mincoverage(args.mincoverage, allelesets[0]) is False:
continue
# Establish first contig
if current_contig is None:
current_contig = contig[:]
if args.windowsize > 0:
while pos > current_position + args.windowsize - 1:
current_position += args.windowsize
# Check if windows are specified.
if not same_window((current_contig, current_position),
(contig, pos), args.windowsize):
data[(current_contig, current_position)] = {
'contig': current_contig,
'position': current_position
}
data_order.append((current_contig, current_position))
all_diff, all_total = pwdistance_function(all_match)
for samplepair in base_matches:
ndiff, ntotal = pwdistance_function(base_matches[samplepair])
taxa = "{};{}".format(sample_labels[samplepair[0]],
sample_labels[samplepair[1]])
data[(current_contig, current_position)].update({
'{};ndiff'.format(taxa):
ndiff + all_diff,
'{};ntotal'.format(taxa):
ntotal + all_total,
'{};dist'.format(taxa):
zerodiv(ndiff + all_diff, ntotal + all_total)
})
if contig != current_contig:
current_contig = contig[:]
current_position = 0
if args.windowsize > 0:
while pos > current_position + args.windowsize - 1:
current_position += args.windowsize
else:
current_position += args.windowsize
if args.emit_counts:
args.qprint("Writing Full Count Table")
for p0, p1 in base_matches:
outfile_emitcounts.write("#{}\t{}\t{}\t{}\n{}\n".format(
p0, p1, current_position, current_contig, "\n".join([
"{} {}".format(x,
(base_matches[(p0, p1)].get(x, 0) +
all_match.get(x, 0)))
for x in set(base_matches[(p0,
p1)]).union(all_match)
])))
base_matches = dict((x, {}) for x in sample_pairs)
all_match = {}
data_in_buffer = False
for iframe in allele_frames:
alleles = allelesets[iframe]
if len(alleles) == 1:
all_match["{0}{0}".format(alleles)] = (
all_match.get("{0}{0}".format(alleles), 0) + 1)
data_in_buffer = True
continue
if alleles[1] == '+':
if alleles[2] in 'X-':
continue
samplepair = (0, int(alleles[3:]))
if any(x not in sample_indices for x in samplepair):
continue
basepair = "{0}{1}".format(alleles[0], alleles[2])
base_matches[samplepair][basepair] = (
base_matches[samplepair].get(basepair, 0) + 1)
data_in_buffer = True
continue
alleles = mvf.decode(alleles)
valid_positions = [
i for i, x in enumerate(alleles)
if x not in 'X-' and i in sample_indices
]
assert len(alleles) == 4
assert alleles[0] not in 'X-', alleles
assert alleles[1] not in 'X-', alleles
for i, j in combinations(valid_positions, 2):
samplepair = (i, j)
basepair = "{0}{1}".format(alleles[i], alleles[j])
base_matches[samplepair][basepair] = (
base_matches[samplepair].get(basepair, 0) + 1)
data_in_buffer = True
# print(base_matches)
if data_in_buffer is True:
print(sum(base_matches[samplepair].values()), base_matches[samplepair],
samplepair)
print(sum(all_match.values()), all_match)
print(sum(base_matches[samplepair].values()) + sum(all_match.values()))
# Check whether, windows, contigs, or total
if args.windowsize == 0:
current_contig = 'TOTAL'
current_position = 0
elif args.windowsize == -1:
current_position = 0
data[(current_contig, current_position)] = {
'contig': current_contig,
'position': current_position
}
data_order.append((current_contig, current_position))
# print("All match")
all_diff, all_total = pwdistance_function(all_match)
print(all_diff, all_total)
for samplepair in base_matches:
ndiff, ntotal = pwdistance_function(base_matches[samplepair])
taxa = "{};{}".format(sample_labels[samplepair[0]],
sample_labels[samplepair[1]])
data[(current_contig, current_position)].update({
'{};ndiff'.format(taxa):
ndiff + all_diff,
'{};ntotal'.format(taxa):
ntotal + all_total,
'{};dist'.format(taxa):
zerodiv(ndiff + all_diff, ntotal + all_total)
})
if args.emit_counts:
args.qprint("Writing Full Count Table")
for p0, p1 in base_matches:
outfile_emitcounts.write("#{}\t{}\t{}\t{}\n{}\n".format(
p0, p1, current_position, current_contig, "\n".join([
"{} {}".format(x, (base_matches[(p0, p1)].get(x, 0) +
all_match.get(x, 0)))
for x in set(base_matches[(p0, p1)]).union(all_match)
])))
args.qprint("Writing Output")
headers = ['contig', 'position']
for samplepair in sample_pairs:
headers.extend([
'{};{};{}'.format(sample_labels[samplepair[0]],
sample_labels[samplepair[1]], x)
for x in ('ndiff', 'ntotal', 'dist')
])
outfile = OutputFile(path=args.out, headers=headers)
for okey in data_order:
outfile.write_entry(data[okey])
if args.emit_counts:
outfile_emitcounts.close()
return ''
def calc_dstat_combinations(args):
"""Calculate genome-wide D-statstics for
all possible trio combinations of samples
and outgroups specified.
"""
mvf = MultiVariantFile(args.mvf, 'read')
data = {}
sample_labels = mvf.get_sample_ids()
if args.outgroup_indices is not None:
outgroup_indices = [
int(x) for x in args.outgroup_indices[0].split(",")
]
elif args.outgroup_labels is not None:
outgroup_indices = mvf.get_sample_indices(
ids=args.outgroup_labels[0].split(","))
if args.sample_indices is not None:
sample_indices = [int(x) for x in args.sample_indices[0].split(",")]
elif args.sample_labels is not None:
sample_indices = mvf.get_sample_indices(
ids=args.sample_labels[0].split(","))
else:
sample_indices = mvf.get_sample_indices()
if args.contig_ids is not None:
contig_ids = args.contig_ids[0].split(",")
elif args.contig_labels is not None:
contig_ids = mvf.get_contig_ids(
labels=args.contig_labels[0].split(","))
else:
contig_ids = None
if any(x in outgroup_indices for x in sample_indices):
raise RuntimeError("Sample and Outgroup column lists cannot overlap.")
for contig, _, allelesets in mvf:
if contig not in contig_ids:
continue
alleles = mvf.decode(allelesets[0])
for i, j, k in combinations(sample_indices, 3):
for outgroup in outgroup_indices:
subset = [alleles[x] for x in [i, j, k, outgroup]]
if any(x not in 'ATGC' for x in subset):
continue
if subset[-1] not in subset[:3]:
continue
if len(set(subset)) != 2:
continue
# [ABBA, BABA, BBAA]
val = (0 + 1 * (subset[0] == subset[3]) + 2 *
(subset[1] == subset[3]) + 4 * (subset[2] == subset[3]))
if val in (1, 2):
val -= 1
elif val == 4:
val = 2
else:
continue
tetrad = (i, j, k, outgroup)
if tetrad not in data:
data[tetrad] = {}
if contig not in data[tetrad]:
data[tetrad][contig] = [0, 0, 0]
data[tetrad][contig][val] += 1
# WRITE OUTPUT
headers = ['sample0', 'sample1', 'sample2', "outgroup"]
for xcontig in contig_ids:
headers.extend([
'{}:abba'.format(xcontig), '{}:baba'.format(xcontig),
'{}:bbaa'.format(xcontig), '{}:D'.format(xcontig)
])
outfile = OutputFile(path=args.out, headers=headers)
for i, j, k in combinations(sample_indices, 3):
for outgroup in outgroup_indices:
tetrad = tuple([i, j, k, outgroup])
if tetrad not in data:
continue
entry = dict(('sample{}'.format(i), sample_labels[x])
for i, x in enumerate(tetrad[:3]))
entry['outgroup'] = sample_labels[outgroup]
for contig in contig_ids:
if contig not in data[tetrad]:
entry.update(dict().fromkeys([
'{}:abba'.format(contig), '{}:baba'.format(contig),
'{}:bbaa'.format(contig), '{}:D'.format(contig)
], '0'))
else:
[abba, baba, bbaa] = data[tetrad][contig]
if abba > baba and abba > bbaa:
dstat = zerodiv(baba - bbaa, baba + bbaa)
elif baba > bbaa and baba > abba:
dstat = zerodiv(abba - bbaa, abba + bbaa)
else:
dstat = zerodiv(abba - baba, abba + baba)
entry.update([('{}:abba'.format(contig), abba),
('{}:baba'.format(contig), baba),
('{}:bbaa'.format(contig), bbaa),
('{}:D'.format(contig), dstat)])
outfile.write_entry(entry)
return ''
def calc_character_count(args):
"""Count the number of and relative rate of certain bases
spatially along chromosomes
"""
mvf = MultiVariantFile(args.mvf, 'read')
data = {}
current_contig = None
current_position = 0
all_match = 0
all_total = 0
data_in_buffer = False
# Set up base matching from special words
data_order = []
def proc_special_word(argx):
if argx == 'dna':
argx = MLIB.validchars['dna']
elif argx == 'dnaambig2':
argx = MLIB.validchars['dna+ambig2']
elif argx == 'dnaambig3':
argx = MLIB.validchars['dna+ambig3']
elif argx == 'dnaambigall':
argx = MLIB.validchars['dna+ambigall']
elif argx == 'prot':
argx = MLIB.validchars['amino']
return argx
args.base_match = proc_special_word(args.base_match)
args.base_total = proc_special_word(args.base_total)
# Set up sample indices
if args.sample_indices is not None:
sample_indices = [int(x) for x in args.sample_indices[0].split(",")]
elif args.sample_labels is not None:
sample_indices = mvf.get_sample_indices(
ids=args.sample_labels[0].split(","))
else:
sample_indices = mvf.get_sample_indices()
sample_labels = mvf.get_sample_ids(indices=sample_indices)
# Set up contig ids
if args.contig_ids is not None:
contig_indices = mvf.get_contig_indices(
ids=args.contig_ids[0].split(","))
elif args.contig_labels is not None:
contig_indices = mvf.get_contig_indices(
labels=args.contig_labels[0].split(","))
else:
contig_indices = None
match_counts = dict().fromkeys([sample_labels[i] for i in sample_indices],
0)
total_counts = dict().fromkeys([sample_labels[i] for i in sample_indices],
0)
for contig, pos, allelesets in mvf.iterentries(
decode=False, contig_indices=contig_indices):
# Check Minimum Site Coverage
if check_mincoverage(args.mincoverage, allelesets[0]) is False:
continue
# if contig not in contig_ids:
# continue
# Establish first contig
if current_contig is None:
current_contig = contig[:]
if args.windowsize > 0:
while pos > current_position + args.windowsize - 1:
current_position += args.windowsize
# Check if windows are specified.
if not same_window((current_contig, current_position),
(contig, pos), args.windowsize):
data[(current_contig, current_position)] = {
'contig': current_contig,
'position': current_position
}
data_order.append((current_contig, current_position))
for k in match_counts:
data[(current_contig, current_position)].update([
(k + '.match', match_counts[k] + all_match),
(k + '.total', total_counts[k] + all_total),
(k + '.prop', ((float(match_counts[k] + all_match) /
float(total_counts[k] + all_total))
if total_counts[k] + all_total > 0 else 0))
])
if contig != current_contig:
current_contig = contig[:]
current_position = 0
else:
current_position += (0 if args.windowsize == -1 else
args.windowsize)
match_counts = dict().fromkeys(
[sample_labels[i] for i in sample_indices], 0)
total_counts = dict().fromkeys(
[sample_labels[i] for i in sample_indices], 0)
all_total = 0
all_match = 0
data_in_buffer = False
else:
alleles = allelesets[0]
if len(alleles) == 1:
if args.base_match is None:
all_match += 1
elif alleles in args.base_match:
all_match += 1
if args.base_total is None:
all_total += 1
elif alleles in args.base_total:
all_total += 1
else:
alleles = mvf.decode(alleles)
for i in sample_indices:
if args.base_match is None:
match_counts[sample_labels[i]] += 1
elif alleles[i] in args.base_match:
match_counts[sample_labels[i]] += 1
if args.base_total is None:
total_counts[sample_labels[i]] += 1
elif alleles[i] in args.base_total:
total_counts[sample_labels[i]] += 1
data_in_buffer = True
if data_in_buffer:
data[(current_contig, current_position)] = {
'contig': current_contig,
'position': current_position
}
data_order.append((current_contig, current_position))
for k in match_counts:
data[(current_contig, current_position)].update([
(k + '.match', match_counts[k] + all_match),
(k + '.total', total_counts[k] + all_total),
(k + '.prop', ((float(match_counts[k] + all_match) /
float(total_counts[k] + all_total))
if total_counts[k] + all_total > 0 else 0))
])
# WRITE OUTPUT
headers = ['contig', 'position']
for label in sample_labels:
headers.extend([label + x for x in ('.match', '.total', '.prop')])
outfile = OutputFile(path=args.out, headers=headers)
for okey in data_order:
outfile.write_entry(data[okey])
return ''
def calc_pattern_count(args):
"""Count biallelic patterns spatially along
chromosomes (e.g,, for use in DFOIL or Dstats
http://www.github.com/jbpease/dfoil).
The last sample specified will determine the 'A'
versus 'B' allele.
"""
mvf = MultiVariantFile(args.mvf, 'read')
data = {}
current_contig = None
current_position = 0
sitepatterns = {}
# sample_labels = mvf.get_sample_labels()
if args.sample_indices is not None:
sample_indices = [int(x) for x in args.sample_indices[0].split(",")]
elif args.sample_labels is not None:
sample_indices = mvf.get_sample_indices(
labels=args.sample_labels[0].split(","))
else:
sample_indices = mvf.get_sample_indices()
nsamples = len(sample_indices)
for contig, pos, allelesets in mvf:
# Check Minimum Site Coverage
if check_mincoverage(args.mincoverage, allelesets[0]) is False:
continue
# Establish first contig
if current_contig is None:
current_contig = contig[:]
if args.windowsize > 0:
while pos > current_position + args.windowsize - 1:
current_position += args.windowsize
# Check if windows are specified.
if not same_window((current_contig, current_position),
(contig, pos), args.windowsize):
data[(current_contig,
current_position)] = dict([('contig', current_contig),
('position', current_position)])
data[(current_contig, current_position)].update(sitepatterns)
sitepatterns = {}
if contig != current_contig:
current_position = 0
current_contig = contig[:]
else:
current_position += (0 if args.windowsize == -1 else
args.windowsize)
if len(allelesets[0]) == 1:
if allelesets[0] in 'ATGC':
pattern = 'A' * nsamples
else:
continue
elif allelesets[0][1] == '+':
continue
else:
alleles = mvf.decode(allelesets[0])
alleles = [alleles[x] for x in sample_indices]
if any(x in alleles for x in 'X-RYKMWS'):
continue
if len(set(alleles)) > 2:
continue
pattern = ''.join(
['A' if x == alleles[-1] else 'B' for x in alleles[:-1]]) + 'A'
sitepatterns[pattern] = sitepatterns.get(pattern, 0) + 1
if sitepatterns:
data[(current_contig,
current_position)] = dict([('contig', current_contig),
('position', current_position)])
data[(current_contig, current_position)].update(sitepatterns)
# WRITE OUTPUT
headers = ['contig', 'position']
headers.extend(
[MLIB.abpattern(x, nsamples) for x in range(0, 2**nsamples, 2)])
outfile = OutputFile(path=args.out, headers=headers)
outfile.write("#{}\n".format(",".join(
mvf.get_sample_labels(sample_indices))))
sorted_entries = sorted([(data[k]['contig'], data[k]['position'], k)
for k in data])
for _, _, k in sorted_entries:
outfile.write_entry(data[k])
# WRITE LIST OUTPUT
if args.output_lists is True:
sorted_entries = sorted([(data[k]['contig'], data[k]['position'], k)
for k in data])
total_counts = {}
for contig, pos, k in sorted_entries:
outfilepath = "{}-{}-{}.counts.list".format(args.out, contig, pos)
with open(outfilepath, 'w') as outfile:
outfile.write("pattern,count\n")
for pattern, pcount in sorted(data[k].items()):
if pattern in ['contig', 'position']:
continue
outfile.write("{},{}\n".format(pattern, pcount))
total_counts[pattern] = (total_counts.get(pattern, 0) +
pcount)
outfilepath = "{}-TOTAL.counts.list".format(args.out)
with open(outfilepath, 'w') as outfile:
outfile.write("pattern,count\n")
for pattern, pcount in sorted(total_counts.items()):
if pattern in ['contig', 'position']:
continue
outfile.write("{},{}\n".format(pattern, pcount))
return ''
def mvf_join(args):
"""Main method"""
concatmvf = MultiVariantFile(args.out, 'write', overwrite=args.overwrite)
# Copy the first file's metadata
if args.main_header_file:
if args.main_header_file not in args.mvf:
raise RuntimeError("{} not found in files".format(
args.main_header_file))
else:
args.main_header_file = args.mvf.index(args.main_header_file)
else:
args.main_header_file = 0
first_mvf = MultiVariantFile(args.mvf[args.main_header_file], 'read')
concatmvf.metadata = first_mvf.metadata.copy()
# Open each MVF file, read headers to make unified header
transformers = []
for mvfname in args.mvf:
# This will create a dictionary of samples{old:new}, contigs{old:new}
transformer = MvfTransformer()
mvf = MultiVariantFile(mvfname, 'read')
for i, label in enumerate(mvf.get_sample_labels()):
if label not in concatmvf.get_sample_labels():
concatmvf.metadata['labels'].append(label)
concatmvf.metadata['samples'][
concatmvf.metadata['labels'].index(label)] = {
'label': label
}
if concatmvf.metadata['labels'].index(label) != i:
transformer.set_label(
i, concatmvf.metadata['labels'].index(label))
for contigid, contigdata in iter(mvf.metadata['contigs'].items()):
if contigdata['label'] not in [
concatmvf.metadata['contigs'][x]['label']
for x in concatmvf.metadata['contigs']
]:
newid = (contigid not in concatmvf.metadata['contigs']
and contigid or concatmvf.get_next_contig_id())
concatmvf.metadata['contigs'][newid] = contigdata
else:
for concatid, concatdata in (
concatmvf.metadata['contigs'].items()):
if contigdata['label'] == concatdata['label']:
newid = concatid
break
if newid != contigid:
transformer.set_contig(contigid, newid)
transformers.append(transformer)
# Write output header
concatmvf.write_data(concatmvf.get_header())
# Now loop through each file
entries = []
nentries = 0
for ifile, mvfname in enumerate(args.mvf):
if not args.quiet:
sys.stderr.write("Processing {} ...\n".format(mvfname))
transformer = transformers[ifile]
mvf = MultiVariantFile(mvfname, 'read')
for contigid, pos, allelesets in mvf.iterentries(decode=False,
quiet=args.quiet):
if transformer.labels:
allelesets = [mvf.decode(x) for x in allelesets]
for j, alleles in enumerate(allelesets):
allelesets[j] = concatmvf.encode(''.join([
x in transformer.labels
and alleles[transformer.labels[x]] or alleles[x]
for x in range(len(alleles))
]))
if transformer.contigs:
contigid = (contigid in transformer['contigs']
and transformer['contigs'][contigid] or contigid)
entries.append((contigid, pos, allelesets))
nentries += 1
if nentries == args.line_buffer:
concatmvf.write_entries(entries)
entries = []
nentries = 0
if entries:
concatmvf.write_entries(entries)
entries = []
nentries = 0
if not args.quiet:
sys.stderr.write("done\n")
return ''
def translate_mvf(args):
"""Main method"""
args.qprint("Running TranslateMVF")
if args.gff:
args.qprint("Reading and Indexing MVF.")
else:
args.qprint("Reading MVF.")
mvf = MultiVariantFile(args.mvf, 'read', contigindex=bool(args.gff))
if mvf.flavor != 'dna':
raise RuntimeError("MVF must be flavor=dna to translate")
if args.gff:
args.qprint("Processing MVF Index File.")
mvf.read_index_file()
args.qprint("GFF processing start.")
gff_genes, gene_order = parse_gff_exome(args)
args.qprint("GFF processed.")
outmvf = MultiVariantFile(args.out, 'write', overwrite=args.overwrite)
outmvf.copy_headers_from(mvf)
outmvf.contig_data = dict(
(
i, dict((y, z)
for (y, z) in gff_genes[x].items()
if y not in ('cds', )))
for (i, x) in enumerate(gene_order))
outmvf.contig_indices = list(range(len(gene_order)))
outmvf.contig_ids = [gff_genes[x]['id']
for x in gene_order]
outmvf.contig_labels = [gff_genes[x]['label']
for x in gene_order]
outmvf.flavor = args.output_data
outmvf.metadata.notes.append(args.command_string)
outmvf.write_data(outmvf.get_header())
args.qprint("Output MVF Established.")
entrybuffer = []
nentry = 0
pos = None
if not args.gff:
args.qprint("No GFF used, translating sequences as pre-aligned in "
"coding frame.")
inputbuffer = []
current_contig = ''
for contigid, pos, allelesets in mvf.iterentries(decode=False):
if current_contig == '':
current_contig = contigid[:]
if contigid == current_contig:
inputbuffer.append((pos, allelesets))
else:
for _, amino_acids, alleles in iter_codons(
inputbuffer, mvf):
if all([x in '-X' for x in amino_acids]):
continue
if args.output_data == 'protein':
entrybuffer.append(
(current_contig, pos, (amino_acids,)))
else:
entrybuffer.append((
current_contig, pos, (
amino_acids, alleles[0],
alleles[1], alleles[2])))
nentry += 1
if nentry == args.line_buffer:
outmvf.write_entries(entrybuffer)
entrybuffer = []
nentry = 0
inputbuffer = [(pos, allelesets)]
current_contig = contigid[:]
if inputbuffer:
for _, amino_acids, alleles in iter_codons(
inputbuffer, outmvf):
if all([x in '-X' for x in amino_acids]):
continue
if args.output_data == 'protein':
entrybuffer.append(
(current_contig, pos, (amino_acids,)))
else:
entrybuffer.append((
current_contig, pos, (
amino_acids, alleles[0],
alleles[1], alleles[2])))
nentry += 1
if nentry == args.line_buffer:
outmvf.write_entries(entrybuffer)
entrybuffer = []
nentry = 0
else:
running_gene_index = -1
for igene, gene in enumerate(gene_order):
xcontiglabel = gff_genes[gene]['contig']
xcontig = mvf.get_contig_indices(
labels=gff_genes[gene]['contig'])
if xcontig is None:
print("Warning: contig {} not found".format(
gff_genes[gene]['contig']))
xcontigid = mvf.get_contig_ids(indices=xcontig)[0]
min_gene_coord = gff_genes[gene]['cds'][0][0]
max_gene_coord = gff_genes[gene]['cds'][-1][1]
mvf_entries = {}
if not igene % 100:
args.qprint("Processing gene {} on {}".format(
gene, xcontiglabel))
for contigid, pos, allelesets in mvf.itercontigentries(
xcontig, decode=False):
if pos < min_gene_coord:
continue
if pos > max_gene_coord:
break
mvf_entries[pos] = allelesets[0]
reverse_strand = gff_genes[gene]['strand'] == '-'
coords = []
running_gene_index += 1
for elem in gff_genes[gene]['cds']:
coords.extend(list(range(elem[0], elem[1] + 1)))
if reverse_strand:
coords = coords[::-1]
for codoncoord in range(0, len(coords), 3):
alleles = tuple(mvf_entries.get(x, '-')
for x in coords[codoncoord:codoncoord + 3])
if len(alleles) < 3:
alleles = tuple(list(alleles) + ['-'] * (3 - len(alleles)))
if all(len(x) == 1 for x in alleles):
if reverse_strand:
alleles = tuple(
MLIB.complement_bases[x] for x in alleles)
decoded_alleles = alleles
amino_acids = translate_single_codon(''.join(alleles))
else:
if reverse_strand is True:
decoded_alleles = tuple(tuple(MLIB.complement_bases[y]
for y in mvf.decode(x))
for x in alleles)
alleles = tuple(outmvf.encode(''.join(x))
for x in decoded_alleles)
else:
decoded_alleles = tuple(mvf.decode(x) for x in alleles)
amino_acids = tuple(translate_single_codon(''.join(x))
for x in zip(*decoded_alleles))
amino_acids = outmvf.encode(''.join(amino_acids))
if args.output_data == 'protein':
entrybuffer.append((
(
xcontigid
if args.retain_contigs
else running_gene_index
),
(
coords[codoncoord]
if args.retain_coords
else codoncoord
),
(
amino_acids,
)
))
elif args.output_data == 'codon':
entrybuffer.append((
(
xcontigid
if args.retain_contigs
else running_gene_index
),
(
coords[codoncoord]
if args.retain_coords
else codoncoord
),
(
amino_acids,
alleles[0],
alleles[1],
alleles[2]
)
))
elif args.output_data == 'dna':
for j, elem in enumerate(
range(codoncoord,
min(codoncoord + 3, len(coords)))):
entrybuffer.append((
(
xcontigid
if args.retain_contigs
else running_gene_index
),
(
coords[elem]
if args.retain_coords
else elem + 1
),
(
alleles[j],
)
))
nentry += 1
if nentry >= args.line_buffer:
args.qprint("Writing a block of {} entries.".format(
args.line_buffer))
outmvf.write_entries(entrybuffer)
entrybuffer = []
nentry = 0
if entrybuffer:
outmvf.write_entries(entrybuffer)
entrybuffer = []
nentry = 0
return ''
def legacy_translate_mvf(args):
"""Main method"""
args.qprint("Running LegacyTranslateMVF")
if args.gff:
args.qprint("Reading and Indexing MVF.")
else:
args.qprint("Reading MVF.")
mvf = MultiVariantFile(args.mvf, 'read', contigindex=bool(args.gff))
if mvf.flavor != 'dna':
raise RuntimeError("MVF must be flavor=dna to translate")
if args.gff:
args.qprint("Processing MVF Index File.")
mvf.read_index_file()
args.qprint("GFF processing start.")
gff = parse_gff_legacy_translate(
args.gff, args,
parent_gene_pattern=args.parent_gene_pattern)
args.qprint("GFF processed.")
outmvf = MultiVariantFile(args.out, 'write', overwrite=args.overwrite)
outmvf.copy_headers_from(mvf)
outmvf.flavor = args.output_data
outmvf.write_data(outmvf.get_header())
args.qprint("Output MVF Established.")
entrybuffer = []
nentry = 0
pos = None
if not args.gff:
args.qprint("No GFF used, translating sequences as pre-aligned in "
"coding frame.")
inputbuffer = []
current_contig = ''
for contigid, pos, allelesets in mvf.iterentries(decode=False):
if current_contig == '':
current_contig = contigid[:]
if contigid == current_contig:
inputbuffer.append((pos, allelesets))
else:
for _, amino_acids, alleles in iter_codons(
inputbuffer, mvf):
if all([x in '-X' for x in amino_acids]):
continue
if args.output_data == 'protein':
entrybuffer.append(
(current_contig, pos, (amino_acids,)))
else:
entrybuffer.append((
current_contig, pos, (
amino_acids, alleles[0],
alleles[1], alleles[2])))
nentry += 1
if nentry == args.line_buffer:
outmvf.write_entries(entrybuffer)
entrybuffer = []
nentry = 0
inputbuffer = [(pos, allelesets)]
current_contig = contigid[:]
if inputbuffer:
for _, amino_acids, alleles in iter_codons(
inputbuffer, outmvf):
if all([x in '-X' for x in amino_acids]):
continue
if args.output_data == 'protein':
entrybuffer.append(
(current_contig, pos, (amino_acids,)))
else:
entrybuffer.append((
current_contig, pos, (
amino_acids, alleles[0],
alleles[1], alleles[2])))
nentry += 1
if nentry == args.line_buffer:
outmvf.write_entries(entrybuffer)
entrybuffer = []
nentry = 0
else:
args.qprint("Indexing GFF gene names.")
# mvfid_to_gffname = outmvf.get_contig_reverse_dict()
for xcontig in outmvf.get_contig_indices():
mvf_entries = {}
xcontiglabel = outmvf.get_contig_labels(indices=xcontig)[0]
xcontigid = outmvf.get_contig_ids(indices=xcontig)[0]
if xcontiglabel not in gff:
if args.verbose:
print(
("No entries in GFF, "
"skipping contig: index:{} id:{} label:{}").format(
xcontig, xcontigid, xcontiglabel))
continue
if not xcontig % 100:
args.qprint("Processing contig: {} {}".format(
xcontigid, xcontiglabel))
for contigid, pos, allelesets in mvf.itercontigentries(
xcontig, decode=False):
mvf_entries[pos] = allelesets[0]
for coords in sorted(gff[xcontiglabel]):
reverse_strand = coords[3] == '-'
alleles = (tuple(mvf_entries.get(x, '-')
for x in coords[2::-1])
if reverse_strand is True
else tuple(mvf_entries.get(x, '-')
for x in coords[0:3]))
if all(len(x) == 1 for x in alleles):
if reverse_strand:
alleles = tuple(
MLIB.complement_bases[x] for x in alleles)
decoded_alleles = alleles
amino_acids = translate_single_codon(''.join(alleles))
else:
if reverse_strand is True:
decoded_alleles = tuple(tuple(MLIB.complement_bases[y]
for y in mvf.decode(x))
for x in alleles)
alleles = tuple(outmvf.encode(''.join(x))
for x in decoded_alleles)
else:
decoded_alleles = tuple(mvf.decode(x) for x in alleles)
amino_acids = tuple(translate_single_codon(''.join(x))
for x in zip(*decoded_alleles))
# print("aminx", amino_acids)
amino_acids = outmvf.encode(''.join(amino_acids))
# if all(x in '-X' for x in amino_acids):
# continue
# print("amino", amino_acids)
# print("translated", amino_acids, alleles)
if args.output_data == 'protein':
entrybuffer.append((xcontig, coords[0], (amino_acids,)))
else:
entrybuffer.append((
xcontigid, coords[0], (
amino_acids, alleles[0], alleles[1], alleles[2])))
nentry += 1
if nentry >= args.line_buffer:
args.qprint("Writing a block of {} entries.".format(
args.line_buffer))
outmvf.write_entries(entrybuffer)
entrybuffer = []
nentry = 0
if entrybuffer:
outmvf.write_entries(entrybuffer)
entrybuffer = []
nentry = 0
return ''
def translate_mvf(args):
"""Main method"""
mvf = MultiVariantFile(args.mvf, 'read')
if mvf.flavor != 'dna':
raise RuntimeError("MVF must be flavor=dna to translate")
if args.gff:
gff = parse_gff_translate(args.gff, args)
if not args.quiet:
print("gff_processed")
outmvf = MultiVariantFile(args.out, 'write', overwrite=args.overwrite)
outmvf.metadata = deepcopy(mvf.metadata)
outmvf.flavor = args.output_data
outmvf.write_data(outmvf.get_header())
entrybuffer = []
nentry = 0
if not args.gff:
inputbuffer = []
current_contig = ''
for contigid, pos, allelesets in mvf.iterentries(decode=False):
if current_contig == '':
current_contig = contigid[:]
if contigid == current_contig:
inputbuffer.append((pos, allelesets))
else:
for _, amino_acids, alleles in iter_codons(inputbuffer, mvf):
if all([x in '-X' for x in amino_acids]):
continue
if args.output_data == 'protein':
entrybuffer.append(
(current_contig, pos, (amino_acids, )))
else:
entrybuffer.append(
(current_contig, pos, (amino_acids, alleles[0],
alleles[1], alleles[2])))
nentry += 1
if nentry == args.line_buffer:
outmvf.write_entries(entrybuffer)
entrybuffer = []
nentry = 0
inputbuffer = [(pos, allelesets)]
current_contig = contigid[:]
if inputbuffer:
for _, amino_acids, alleles in iter_codons(inputbuffer, mvf):
if all([x in '-X' for x in amino_acids]):
continue
if args.output_data == 'protein':
entrybuffer.append((current_contig, pos, (amino_acids, )))
else:
entrybuffer.append(
(current_contig, pos, (amino_acids, alleles[0],
alleles[1], alleles[2])))
nentry += 1
if nentry == args.line_buffer:
outmvf.write_entries(entrybuffer)
entrybuffer = []
nentry = 0
else:
mvf_entries = {}
for contigid, pos, allelesets in mvf.iterentries(decode=False):
if contigid not in mvf_entries:
mvf_entries[contigid] = {}
mvf_entries[contigid][pos] = allelesets[0]
for contigname in sorted(gff):
contigid = mvf.get_contig_ids(labels=contigname)[0]
for coords in sorted(gff[contigname]):
reverse_strand = False
if coords[3] == '-':
reverse_strand = True
alleles = [
mvf_entries[contigid].get(x, '-')
for x in coords[2::-1]
]
else:
alleles = [
mvf_entries[contigid].get(x, '-') for x in coords[0:3]
]
if all(len(x) == 1 for x in alleles):
if reverse_strand:
alleles = [MLIB.complement_bases[x] for x in alleles]
decoded_alleles = alleles
amino_acids = translate(''.join(alleles))[0]
else:
if reverse_strand:
decoded_alleles = [[
MLIB.complement_bases[y] for y in mvf.decode(x)
] for x in alleles]
alleles = [
mvf.encode(''.join(x)) for x in decoded_alleles
]
else:
decoded_alleles = [mvf.decode(x) for x in alleles]
amino_acids = [
translate(''.join(x)) for x in zip(*decoded_alleles)
]
amino_acids = mvf.encode(''.join(
[x[0] for x in amino_acids]))
if all([x in '-X' for x in amino_acids]):
continue
if args.output_data == 'protein':
entrybuffer.append((contigid, coords[0], (amino_acids, )))
else:
entrybuffer.append(
(contigid, coords[0], (amino_acids, alleles[0],
alleles[1], alleles[2])))
nentry += 1
if nentry == args.line_buffer:
outmvf.write_entries(entrybuffer)
entrybuffer = []
nentry = 0
if entrybuffer:
outmvf.write_entries(entrybuffer)
entrybuffer = []
nentry = 0
return ''
def calc_pairwise_distances(args):
"""Count the pairwise nucleotide distance between
combinations of samples in a window
"""
mvf = MultiVariantFile(args.mvf, 'read')
data = {}
sample_labels = mvf.get_sample_labels()
if args.sample_indices is not None:
sample_indices = [int(x) for x in
args.sample_indices[0].split(",")]
elif args.sample_labels is not None:
sample_indices = mvf.get_sample_indices(
labels=args.sample_labels[0].split(","))
else:
sample_indices = mvf.get_sample_indices()
current_contig = None
current_position = 0
data_in_buffer = False
sample_pairs = [tuple(x) for x in combinations(sample_indices, 2)]
base_matches = dict([(x, {}) for x in sample_pairs])
all_match = {}
for contig, pos, allelesets in mvf:
# Check Minimum Site Coverage
if check_mincoverage(args.mincoverage, allelesets[0]) is False:
continue
# Establish first contig
if current_contig is None:
current_contig = contig[:]
while pos > current_position + args.windowsize - 1:
current_position += args.windowsize
# Check if windows are specified.
if not same_window((current_contig, current_position),
(contig, pos), args.windowsize):
data[(current_contig, current_position)] = {
'contig': current_contig, 'position': current_position}
if mvf.flavor == 'dna':
all_diff, all_total = pairwise_distance_nuc(all_match)
elif mvf.flavor == 'prot':
all_diff, all_total = pairwise_distance_prot(all_match)
for samplepair in base_matches:
if mvf.flavor == 'dna':
ndiff, ntotal = pairwise_distance_nuc(
base_matches[samplepair])
elif mvf.flavor == 'prot':
ndiff, ntotal = pairwise_distance_prot(
base_matches[samplepair])
taxa = "{};{}".format(sample_labels[samplepair[0]],
sample_labels[samplepair[1]])
data[(current_contig, current_position)].update({
'{};ndiff'.format(taxa): ndiff + all_diff,
'{};ntotal'.format(taxa): ntotal + all_total,
'{};dist'.format(taxa): zerodiv(ndiff + all_diff,
ntotal + all_total)})
if contig != current_contig:
current_contig = contig[:]
current_position = 0
while pos > current_position + args.windowsize - 1:
current_position += args.windowsize
else:
current_position += args.windowsize
base_matches = dict([(x, {}) for x in sample_pairs])
all_match = {}
data_in_buffer = False
alleles = allelesets[0]
if len(alleles) == 1:
all_match["{}{}".format(alleles, alleles)] = (
all_match.get("{}{}".format(alleles, alleles),
0) + 1)
data_in_buffer = True
continue
if alleles[1] == '+':
if 'X' in alleles or '-' in alleles:
continue
samplepair = (0, int(alleles[3:]))
if any(x not in sample_indices for x in samplepair):
continue
basepair = "{}{}".format(alleles[0], alleles[2])
base_matches[samplepair][basepair] = (
base_matches[samplepair].get(basepair, 0) + 1)
data_in_buffer = True
continue
alleles = mvf.decode(alleles)
valid_positions = [i for i, x in enumerate(alleles)
if x not in 'X-']
for i, j in combinations(valid_positions, 2):
samplepair = (i, j)
if any(x not in sample_indices for x in samplepair):
continue
basepair = "{}{}".format(alleles[i], alleles[j])
base_matches[samplepair][basepair] = (
base_matches[samplepair].get(basepair, 0) + 1)
data_in_buffer = True
if data_in_buffer is True:
# Check whether, windows, contigs, or total
if args.windowsize == 0:
current_contig = 'TOTAL'
current_position = 0
elif args.windowsize == -1:
current_position = 0
data[(current_contig, current_position)] = {
'contig': current_contig, 'position': current_position}
if mvf.flavor == 'dna':
all_diff, all_total = pairwise_distance_nuc(all_match)
elif mvf.flavor == 'prot':
all_diff, all_total = pairwise_distance_prot(all_match)
for samplepair in base_matches:
if mvf.flavor == 'dna':
ndiff, ntotal = pairwise_distance_nuc(base_matches[samplepair])
elif mvf.flavor == 'prot':
ndiff, ntotal = pairwise_distance_prot(
base_matches[samplepair])
taxa = "{};{}".format(sample_labels[samplepair[0]],
sample_labels[samplepair[1]])
data[(current_contig, current_position)].update({
'{};ndiff'.format(taxa): ndiff + all_diff,
'{};ntotal'.format(taxa): ntotal + all_total,
'{};dist'.format(taxa): zerodiv(ndiff + all_diff,
ntotal + all_total)})
headers = ['contig', 'position']
for samplepair in sample_pairs:
headers.extend(['{};{};{}'.format(
sample_labels[samplepair[0]],
sample_labels[samplepair[1]],
x) for x in ('ndiff', 'ntotal', 'dist')])
outfile = OutputFile(path=args.out, headers=headers)
sorted_entries = sorted([(
data[k]['contig'], data[k]['position'], k)
for k in data])
for _, _, k in sorted_entries:
outfile.write_entry(data[k])
return ''
def calc_character_count(args):
"""Count the number of and relative rate of certain bases
spatially along chromosomes
"""
mvf = MultiVariantFile(args.mvf, 'read')
data = {}
current_contig = None
current_position = 0
all_match = 0
all_total = 0
data_in_buffer = 0
# Set up sample indices
sample_labels = mvf.get_sample_labels()
if args.sample_indices is not None:
sample_indices = [int(x) for x in
args.sample_indices[0].split(",")]
elif args.sample_labels is not None:
sample_indices = mvf.get_sample_indices(
labels=args.sample_labels[0].split(","))
else:
sample_indices = mvf.get_sample_indices()
# Set up contig ids
if args.contig_ids is not None:
contig_ids = args.contig_ids[0].split(",")
elif args.contig_labels is not None:
contig_ids = mvf.get_contig_ids(
labels=args.contig_labels[0].split(","))
else:
contig_ids = mvf.get_contig_ids()
match_counts = dict().fromkeys(
[sample_labels[i] for i in sample_indices], 0)
total_counts = dict().fromkeys(
[sample_labels[i] for i in sample_indices], 0)
for contig, pos, allelesets in mvf:
# Check Minimum Site Coverage
if check_mincoverage(args.mincoverage,
allelesets[0]) is False:
continue
if contig not in contig_ids:
continue
# Establish first contig
if current_contig is None:
current_contig = contig[:]
while pos > current_position + args.windowsize - 1:
current_position += args.windowsize
# Check if windows are specified.
if not same_window((current_contig, current_position),
(contig, pos), args.windowsize):
data[(current_contig, current_position)] = {
'contig': current_contig, 'position': current_position}
for k in match_counts:
data[(current_contig, current_position)].update([
(k + '.match', match_counts[k] + all_match),
(k + '.total', total_counts[k] + all_total),
(k + '.prop', (
(float(match_counts[k] + all_match) /
float(total_counts[k] + all_total)) if
total_counts[k] + all_total > 0 else 0))])
if contig != current_contig:
current_contig = contig[:]
current_position = 0
else:
current_position += (0 if args.windowsize == -1
else args.windowsize)
match_counts = dict().fromkeys(
[sample_labels[i] for i in sample_indices], 0)
total_counts = dict().fromkeys(
[sample_labels[i] for i in sample_indices], 0)
all_total = 0
all_match = 0
data_in_buffer = 0
else:
alleles = allelesets[0]
if len(alleles) == 1:
if args.base_match is None:
all_match += 1
elif alleles in args.base_match:
all_match += 1
if args.base_total is None:
all_total += 1
elif alleles in args.base_total:
all_total += 1
else:
alleles = mvf.decode(alleles)
for i in sample_indices:
if args.base_match is None:
match_counts[sample_labels[i]] += 1
elif alleles[i] in args.base_match:
match_counts[sample_labels[i]] += 1
if args.base_total is None:
total_counts[sample_labels[i]] += 1
elif alleles[i] in args.base_total:
total_counts[sample_labels[i]] += 1
data_in_buffer = 1
if data_in_buffer:
data[(current_contig, current_position)] = {
'contig': current_contig, 'position': current_position}
for k in match_counts:
data[(current_contig, current_position)].update([
(k + '.match', match_counts[k] + all_match),
(k + '.total', total_counts[k] + all_total),
(k + '.prop', ((float(match_counts[k] + all_match) /
float(total_counts[k] + all_total)) if
total_counts[k] + all_total > 0 else 0))])
# WRITE OUTPUT
headers = ['contig', 'position']
for label in sample_labels:
headers.extend([label + x for x in ('.match', '.total', '.prop')])
outfile = OutputFile(path=args.out,
headers=headers)
sorted_entries = sorted([(data[k]['contig'],
data[k]['position'], k)
for k in data])
for _, _, k in sorted_entries:
outfile.write_entry(data[k])
return ''
