def shift_mapco(mapco, refname, region):
'''Shift coordinate map to the beginning of the reference sequence'''
from hivwholeseq.reference import load_custom_reference
refseq = load_custom_reference(refname, format='gb')
for feature in refseq.features:
if feature.id == region:
startref = feature.location.nofuzzy_start
mapco[:, 0] += startref
break
def annotate_like_HXB2(refname, VERBOSE=0):
'''Annotate copying from HXB2'''
hxb2 = load_custom_reference('HXB2', 'gb')
ref = load_custom_reference(refname, 'fasta')
refs = str(ref.seq)
def get_sublocation(sublocation):
hxb2_seq = sublocation.extract(hxb2)
ref_seq = trim_to_refseq(refs, hxb2_seq).replace('-', '')
start = refs.find(ref_seq)
end = start + len(ref_seq)
return FeatureLocation(start, end, strand=+1)
for fea in hxb2.features:
if VERBOSE >= 1:
print fea.id
loc = [get_sublocation(loc) for loc in fea.location.parts]
if len(loc) == 1:
loc = loc[0]
else:
loc = CompoundLocation(loc)
feature = SeqFeature(loc, type=fea.type, id=fea.id)
# Test length of old and new
if fea.id not in ["LTR5'", "LTR3'", 'V4']:
L1 = len(fea.extract(hxb2))
L2 = len(feature.extract(ref))
s = str(L2) + ' vs ' + str(L1)
if 1.0 * L2 / L1 < 0.9:
raise ValueError('Feature: ' + fea.id + ' is too short: ' + s)
elif 1.0 * L2 / L1 > 1.1:
raise ValueError('Feature: ' + fea.id + ' is too long: ' + s)
ref.features.append(feature)
return ref
def annotate_like_HXB2(refname, VERBOSE=0):
'''Annotate copying from HXB2'''
hxb2 = load_custom_reference('HXB2', 'gb')
ref = load_custom_reference(refname, 'fasta')
refs = str(ref.seq)
def get_sublocation(sublocation):
hxb2_seq = sublocation.extract(hxb2)
ref_seq = trim_to_refseq(refs, hxb2_seq).replace('-', '')
start = refs.find(ref_seq)
end = start + len(ref_seq)
return FeatureLocation(start, end, strand=+1)
for fea in hxb2.features:
if VERBOSE >= 1:
print fea.id
loc = [get_sublocation(loc) for loc in fea.location.parts]
if len(loc) == 1:
loc = loc[0]
else:
loc = CompoundLocation(loc)
feature = SeqFeature(loc, type=fea.type, id=fea.id)
# Test length of old and new
if fea.id not in ["LTR5'", "LTR3'", 'V4']:
L1 = len(fea.extract(hxb2))
L2 = len(feature.extract(ref))
s = str(L2)+' vs '+str(L1)
if 1.0 * L2 / L1 < 0.9:
raise ValueError('Feature: '+fea.id+' is too short: '+s)
elif 1.0 * L2 / L1 > 1.1:
raise ValueError('Feature: '+fea.id+' is too long: '+s)
ref.features.append(feature)
return ref
def build_reference_alignments(region, refname,
VERBOSE=0,
subtypes=['B', 'C', 'A', 'AE', 'F1', 'D', 'O', 'H'],
codon_align=False,
require_full_cover=True,
):
'''Build reference alignment by subtype'''
from hivwholeseq.reference import load_custom_reference
from Bio import SeqIO
from Bio.Align import MultipleSeqAlignment
ref = load_custom_reference(refname, region=region)
refstr = ''.join(ref)
seq_by_subtype = defaultdict(list)
fn_in = get_raw_LANL_sequences_filename(region)
if VERBOSE >= 2:
print fn_in
seq_iter = SeqIO.parse(fn_in, 'fasta')
for i, seq in enumerate(seq_iter):
if VERBOSE >= 1:
if not ((i+1) % 100):
print i+1
subtype = seq.id.split('.')[0]
if subtype not in subtypes:
continue
if VERBOSE >= 3:
print subtype
try:
rec = align_to_reference(seq, refstr, VERBOSE=VERBOSE,
require_full_cover=require_full_cover,
codon_align=codon_align)
except ValueError:
continue
seq_by_subtype[subtype].append(rec)
for subtype, seqs in seq_by_subtype.iteritems():
seq_by_subtype[subtype] = MultipleSeqAlignment(seqs)
return seq_by_subtype
def get_gene_HXB2(genename):
'''Get a gene or exon in HXB2'''
from operator import attrgetter
from hivwholeseq.reference import load_custom_reference
HXB2 = load_custom_reference('HXB2', format='gb')
if genename not in ('tat1', 'tat2', 'rev1', 'rev2'):
gene_coord = HXB2.features[map(attrgetter('id'), HXB2.features).index(genename)]
gene_HXB2 = gene_coord.extract(HXB2)
return gene_HXB2
else:
exon_n = int(genename[-1])
genename = genename[:-1]
gene_coord = HXB2.features[map(attrgetter('id'), HXB2.features).index(genename)]
exon_coord = gene_coord.location.parts[exon_n - 1]
exon_HXB2 = exon_coord.extract(HXB2)
return exon_HXB2
def get_gene_HXB2(genename):
'''Get a gene or exon in HXB2'''
from operator import attrgetter
from hivwholeseq.reference import load_custom_reference
HXB2 = load_custom_reference('HXB2', format='gb')
if genename not in ('tat1', 'tat2', 'rev1', 'rev2'):
gene_coord = HXB2.features[map(attrgetter('id'),
HXB2.features).index(genename)]
gene_HXB2 = gene_coord.extract(HXB2)
return gene_HXB2
else:
exon_n = int(genename[-1])
genename = genename[:-1]
gene_coord = HXB2.features[map(attrgetter('id'),
HXB2.features).index(genename)]
exon_coord = gene_coord.location.parts[exon_n - 1]
exon_HXB2 = exon_coord.extract(HXB2)
return exon_HXB2
def check_protein(fea, seqgw, VERBOSE=0, delta_pos=2.5):
'''Check a protein annotation'''
seq = fea.extract(seqgw).seq
if len(seq) % 3:
raise ValueError('The length of ' + fea.id + ' is not a multiple of 3')
if 'N' in seq:
raise ValueError('N nucleotides found in ' + fea.id)
if '-' in seq:
raise ValueError('Gaps found in ' + fea.id)
prot = seq.translate()
if ('*' in prot) and (prot.find('*') != len(prot) - 1):
raise ValueError('Premature stops found in ' + fea.id)
if 'X' in prot:
raise ValueError('X amino acids found in ' + fea.id)
# Compare to HXB2
from hivwholeseq.reference import load_custom_reference
ref = load_custom_reference('HXB2', region=fea.id)
from seqanpy import align_global
(score, alis, alir) = align_global(seq, ref, score_gapopen=-20)
if VERBOSE >= 3:
from hivwholeseq.utils.sequence import pretty_print_pairwise_ali
pretty_print_pairwise_ali((alir, alis),
name1='HXB2',
name2='seq',
width=100)
scoremax = 3 * len(alis)
delta = scoremax - score
if delta > delta_pos * len(alis):
raise ValueError('The sequence of ' + fea.id +
' looks different from HXB2')
def check_protein(fea, seqgw, VERBOSE=0, delta_pos=2.5):
'''Check a protein annotation'''
seq = fea.extract(seqgw).seq
if len(seq) % 3:
raise ValueError('The length of '+fea.id+' is not a multiple of 3')
if 'N' in seq:
raise ValueError('N nucleotides found in '+fea.id)
if '-' in seq:
raise ValueError('Gaps found in '+fea.id)
prot = seq.translate()
if ('*' in prot) and (prot.find('*') != len(prot) - 1):
raise ValueError('Premature stops found in '+fea.id)
if 'X' in prot:
raise ValueError('X amino acids found in '+fea.id)
# Compare to HXB2
from hivwholeseq.reference import load_custom_reference
ref = load_custom_reference('HXB2', region=fea.id)
from seqanpy import align_global
(score, alis, alir) = align_global(seq, ref, score_gapopen=-20)
if VERBOSE >= 3:
from hivwholeseq.utils.sequence import pretty_print_pairwise_ali
pretty_print_pairwise_ali((alir, alis), name1='HXB2', name2='seq',
width=100)
scoremax = 3 * len(alis)
delta = scoremax - score
if delta > delta_pos * len(alis):
raise ValueError('The sequence of '+fea.id+' looks different from HXB2')
maps_coord = defaultdict(dict)
for pname, patient in patients.iterrows():
patient = Patient(patient)
# Make maps for all annotations if not explicit
if regions is None:
patseqann = patient.get_reference('genomewide', format='gb')
regionspat = map(attrgetter('id'), patseqann.features) + ['genomewide']
else:
regionspat = regions
for region in regionspat:
if VERBOSE >= 1:
print pname, region
refseq = load_custom_reference(refname, format='gb', region=region)
patseq = patient.get_reference(region)
mapco = build_coordinate_map(refseq, patseq, VERBOSE=VERBOSE)
mapco = np.array(mapco, int)
shift_mapco(mapco, refname, region)
maps_coord[(region, pname)] = mapco
if save_to_file:
out_fn = get_coordinate_map_filename(pname, region, refname=refname)
np.savetxt(out_fn, mapco, fmt='%d',
delimiter='\t',
header=refname+'\t'+pname+'_'+region)
if VERBOSE:
print 'Saved to file:', pname, region
bins = np.exp(tbins)/(1+np.exp(tbins))
binsc = np.sqrt(bins[1:] * bins[:-1])
binw = np.diff(bins)
hists = np.zeros((len(S_bins) - 1, len(binsc)))
if VERBOSE >= 1:
print 'Load alignment, reference, and coordinate map'
ali = load_custom_alignment('HIV1_FLT_2013_genome_DNA')
alim = np.array(ali, 'S1')
S = np.zeros(alim.shape[1])
for a in alpha[:5]:
nu = (alim == a).mean(axis=0)
S -= nu * np.log2(nu + 1e-8)
refname = 'HXB2'
refseq = load_custom_reference('HXB2', format='gb')
mapali = get_coordinate_map(refname, ali)
if len(refseq) != mapali[0, -1] + 1:
raise ValueError('Reference '+refname+' in alignment is not complete')
Sref = S[mapali[1]]
Srefind = - np.ones(len(Sref), int)
for i, Sbin in enumerate(S_bins[:-2]):
ind = (Sref >= Sbin) & (Sref < S_bins[i + 1])
Srefind[ind] = i
Srefind[Srefind < 0] = len(S_bins) - 2
if not fragments:
fragments = ['F'+str(i) for i in xrange(1, 7)]
if VERBOSE >= 2:
print 'fragments', fragments
edges_chunk = all_edges[name]
edges = find_region_edges(smat, edges_chunk)
# Some features must be stripped of primers
if name in ['V3']:
edges[0] += len(edges_chunk[0])
edges[1] -= len(edges_chunk[1])
return [edges]
# Script
if __name__ == '__main__':
seqold = load_custom_reference('HXB2', 'gb')
seqnew = load_custom_reference('HXB2', 'fasta')
smat = np.array(seqnew)
print 'Add features'
for typ, coord_typ in coordinates.iteritems():
for name, edges in coord_typ.iteritems():
# If coordinates are missing, grab primers
if edges is None:
edges = get_coordinates_feature(smat, name)
if len(edges) == 1:
fea = SeqFeature(FeatureLocation(edges[0][0], edges[0][1], strand=+1),
type=typ,
id=name)
regions = args.regions
VERBOSE = args.verbose
use_save = args.save
use_joint = args.joint
patients = load_patients()
if pnames is not None:
patients = patients.iloc[patients.index.isin(pnames)]
pcodes = [p.code for _, p in patients.iterrows()]
for region in regions:
if use_joint:
seqs_all = []
for refname in _refs:
seq = load_custom_reference(refname, region=region)
if refname == 'F10':
refname = 'pZM246F_10'
seq.id = 'Isolate_'+refname
seq.name = 'Isolate_'+refname
seq.description = 'Isolate: '+refname
if refname in ['38540', 'pZM246F_10']:
seq.subtype = 'C'
else:
seq.subtype = 'B'
seqs_all.append(seq)
for pname, patient in iterpatient(patients):
patient.discard_nonsequenced_samples()
if VERBOSE >= 1:
print region, pname
else:
PCRs_sample = [PCR]
for PCR_sample in PCRs_sample:
bamfilename = sample.get_mapped_filtered_filename(fragment, PCR=PCR_sample, decontaminated=False)
if not os.path.isfile(bamfilename):
continue
# if check_already_decontaminated(sample, fragment, PCR_sample):
# continue
fork_self(samplename, fragment, VERBOSE=VERBOSE, maxreads=maxreads, summary=summary, PCR=PCR_sample)
sys.exit()
for fragment in fragments:
consensi = {refname: "".join(load_custom_reference(refname + "_" + fragment)) for refname in refnames}
for samplename, sample in samples.iterrows():
sample = SamplePat(sample)
try:
consensi[samplename] = sample.get_consensus(fragment, PCR=1)
except IOError:
print samplename, "file not found"
continue
for samplename, sample in samples_focal.iterrows():
sample = SamplePat(sample)
pname = sample.patient
if PCR is None:
PCRs_sample = (1, 2)
else:
#if check_already_decontaminated(sample, fragment, PCR_sample):
# continue
fork_self(samplename,
fragment,
VERBOSE=VERBOSE,
maxreads=maxreads,
summary=summary,
PCR=PCR_sample)
sys.exit()
for fragment in fragments:
consensi = {
refname: ''.join(load_custom_reference(refname + '_' + fragment))
for refname in refnames
}
for samplename, sample in samples.iterrows():
sample = SamplePat(sample)
try:
consensi[samplename] = sample.get_consensus(fragment, PCR=1)
except IOError:
print samplename, 'file not found'
continue
for samplename, sample in samples_focal.iterrows():
sample = SamplePat(sample)
pname = sample.patient
if PCR is None:
for pname, patient in patients.iterrows():
patient = Patient(patient)
# Make maps for all annotations if not explicit
if regions is None:
patseqann = patient.get_reference('genomewide', format='gb')
regionspat = map(attrgetter('id'),
patseqann.features) + ['genomewide']
else:
regionspat = regions
for region in regionspat:
if VERBOSE >= 1:
print pname, region
refseq = load_custom_reference(refname, format='gb', region=region)
patseq = patient.get_reference(region)
mapco = build_coordinate_map(refseq, patseq, VERBOSE=VERBOSE)
mapco = np.array(mapco, int)
shift_mapco(mapco, refname, region)
maps_coord[(region, pname)] = mapco
if save_to_file:
out_fn = get_coordinate_map_filename(pname,
region,
refname=refname)
np.savetxt(out_fn,
mapco,
fmt='%d',
regions = args.regions
VERBOSE = args.verbose
use_save = args.save
use_joint = args.joint
patients = load_patients()
if pnames is not None:
patients = patients.iloc[patients.index.isin(pnames)]
pcodes = [p.code for _, p in patients.iterrows()]
for region in regions:
if use_joint:
seqs_all = []
for refname in _refs:
seq = load_custom_reference(refname, region=region)
if refname == 'F10':
refname = 'pZM246F_10'
seq.id = 'Isolate_' + refname
seq.name = 'Isolate_' + refname
seq.description = 'Isolate: ' + refname
if refname in ['38540', 'pZM246F_10']:
seq.subtype = 'C'
else:
seq.subtype = 'B'
seqs_all.append(seq)
for pname, patient in iterpatient(patients):
patient.discard_nonsequenced_samples()
if VERBOSE >= 1:
print region, pname
def annotate_sequence(seqrecord, additional_edges={}, additional_features=['chunk'], VERBOSE=0):
'''Annotate a consensus with the genes and stuff (in place)'''
# TODO: what do we do with genes that do not start/end where they are
# supposed to? Do we follow biology and track their new locations?
from Bio.SeqFeature import SeqFeature, FeatureLocation, CompoundLocation
from hivwholeseq.utils.genome_info import gene_edges, RNA_structure_edges, \
other_edges, find_region_edges, find_region_edges_multiple, \
locate_gene
edge_dict = {'gene': gene_edges,
'RNA structure': RNA_structure_edges,
'other': other_edges}
edge_dict.update(additional_edges)
additional_features = ['protein'] + additional_features
features = edge_dict.keys() + additional_features
if VERBOSE:
print 'Features:', ', '.join(features)
smat = np.array(seqrecord)
for feature_type in edge_dict:
edges_all = edge_dict[feature_type]
print feature_type, edge_dict[feature_type].keys()
for name, edges in edges_all.iteritems():
if VERBOSE >= 2:
print name,
# Skip a feature if it's present already
if name in map(lambda x: x.id, seqrecord.features):
if VERBOSE >= 2:
print 'already present.'
continue
# Behave differently for unsplit regions and split ones
if len(edges) == 2:
# LTR problems with F6
if 'F6' in name:
pos_edge = find_region_edges(smat[6000::], [edges[0], None])
pos_edge[0] += 6000
elif feature_type == 'genes':
pos_edge = locate_gene(smat, name, output_compact=True)
else:
pos_edge = find_region_edges(smat, edges)
# Cut the primers for some features
if (None not in pos_edge) and name in ['V1', 'V3', 'V4', 'V5']:
pos_edge[0] += len(edges[0])
pos_edge[1] -= len(edges[1])
# Cut only the right primer for V2
if (None not in pos_edge) and name in ['V2']:
pos_edge[1] -= len(edges[1])
if pos_edge[0] is None:
if name not in ['F1', "LTR5'"]:
print 'WARNING: start not found'
pos_edge[0] = 0
if pos_edge[1] is None:
if name not in ['F6', "LTR3'"]:
print 'WARNING: end not found'
pos_edge[1] = len(smat)
location = FeatureLocation(*pos_edge)
else:
if feature_type == 'genes':
pos_edges = [locate_gene(smat, name+suff, output_compact=True)
for suff in ('1', '2')]
else:
pos_edges = find_region_edges_multiple(smat, edges, min_distance=1)
locations = [FeatureLocation(*pos_edge) for pos_edge in pos_edges]
location = CompoundLocation(locations)
if VERBOSE >= 2:
print 'found:', location
feature = SeqFeature(location, type=feature_type, id=name, strand=1)
seqrecord.features.append(feature)
# Add proteins and other features from HXB2
from operator import attrgetter
from seqanpy import align_overlap
from hivwholeseq.utils.genome_info import proteins, chunks
from hivwholeseq.reference import load_custom_reference
additional_features_dict = {}
if 'protein' in additional_features:
additional_features_dict['protein'] = proteins
if 'chunk' in additional_features:
additional_features_dict['chunk'] = chunks
ref_ann = load_custom_reference('HXB2', 'gb')
for feagroup, additional_features_grp in additional_features_dict.iteritems():
for feaname in additional_features_grp:
if VERBOSE >= 2:
print feaname,
fea = ref_ann.features[map(attrgetter('id'), ref_ann.features).index(feaname)]
seq = fea.extract(ref_ann)
(score, ali1, ali2) = align_overlap(seqrecord, seq, score_gapopen=-20)
start = len(ali2) - len(ali2.lstrip('-'))
end = len(ali2.rstrip('-'))
end -= ali1[start: end].count('-')
location = FeatureLocation(start, end)
if VERBOSE >= 2:
print 'found:', location
feature = SeqFeature(location, type=feagroup, id=feaname, strand=1)
seqrecord.features.append(feature)
def annotate_sequence(seqrecord,
additional_edges={},
additional_features=['chunk'],
VERBOSE=0):
'''Annotate a consensus with the genes and stuff (in place)'''
# TODO: what do we do with genes that do not start/end where they are
# supposed to? Do we follow biology and track their new locations?
from Bio.SeqFeature import SeqFeature, FeatureLocation, CompoundLocation
from hivwholeseq.utils.genome_info import gene_edges, RNA_structure_edges, \
other_edges, find_region_edges, find_region_edges_multiple, \
locate_gene
edge_dict = {
'gene': gene_edges,
'RNA structure': RNA_structure_edges,
'other': other_edges
}
edge_dict.update(additional_edges)
additional_features = ['protein'] + additional_features
features = edge_dict.keys() + additional_features
if VERBOSE:
print 'Features:', ', '.join(features)
smat = np.array(seqrecord)
for feature_type in edge_dict:
edges_all = edge_dict[feature_type]
print feature_type, edge_dict[feature_type].keys()
for name, edges in edges_all.iteritems():
if VERBOSE >= 2:
print name,
# Skip a feature if it's present already
if name in map(lambda x: x.id, seqrecord.features):
if VERBOSE >= 2:
print 'already present.'
continue
# Behave differently for unsplit regions and split ones
if len(edges) == 2:
# LTR problems with F6
if 'F6' in name:
pos_edge = find_region_edges(smat[6000::],
[edges[0], None])
pos_edge[0] += 6000
elif feature_type == 'genes':
pos_edge = locate_gene(smat, name, output_compact=True)
else:
pos_edge = find_region_edges(smat, edges)
# Cut the primers for some features
if (None not in pos_edge) and name in ['V1', 'V3', 'V4', 'V5']:
pos_edge[0] += len(edges[0])
pos_edge[1] -= len(edges[1])
# Cut only the right primer for V2
if (None not in pos_edge) and name in ['V2']:
pos_edge[1] -= len(edges[1])
if pos_edge[0] is None:
if name not in ['F1', "LTR5'"]:
print 'WARNING: start not found'
pos_edge[0] = 0
if pos_edge[1] is None:
if name not in ['F6', "LTR3'"]:
print 'WARNING: end not found'
pos_edge[1] = len(smat)
location = FeatureLocation(*pos_edge)
else:
if feature_type == 'genes':
pos_edges = [
locate_gene(smat, name + suff, output_compact=True)
for suff in ('1', '2')
]
else:
pos_edges = find_region_edges_multiple(smat,
edges,
min_distance=1)
locations = [
FeatureLocation(*pos_edge) for pos_edge in pos_edges
]
location = CompoundLocation(locations)
if VERBOSE >= 2:
print 'found:', location
feature = SeqFeature(location,
type=feature_type,
id=name,
strand=1)
seqrecord.features.append(feature)
# Add proteins and other features from HXB2
from operator import attrgetter
from seqanpy import align_overlap
from hivwholeseq.utils.genome_info import proteins, chunks
from hivwholeseq.reference import load_custom_reference
additional_features_dict = {}
if 'protein' in additional_features:
additional_features_dict['protein'] = proteins
if 'chunk' in additional_features:
additional_features_dict['chunk'] = chunks
ref_ann = load_custom_reference('HXB2', 'gb')
for feagroup, additional_features_grp in additional_features_dict.iteritems(
):
for feaname in additional_features_grp:
if VERBOSE >= 2:
print feaname,
fea = ref_ann.features[map(attrgetter('id'),
ref_ann.features).index(feaname)]
seq = fea.extract(ref_ann)
(score, ali1, ali2) = align_overlap(seqrecord,
seq,
score_gapopen=-20)
start = len(ali2) - len(ali2.lstrip('-'))
end = len(ali2.rstrip('-'))
end -= ali1[start:end].count('-')
location = FeatureLocation(start, end)
if VERBOSE >= 2:
print 'found:', location
feature = SeqFeature(location, type=feagroup, id=feaname, strand=1)
seqrecord.features.append(feature)
help='Reference to use for alignment')
parser.add_argument('--verbose',
type=int,
default=0,
help='Verbosity level [0-4]')
parser.add_argument('--subtypes',
nargs='+',
default=['B'],
help='Subtypes to keep')
args = parser.parse_args()
regions = args.regions
refname = args.reference
VERBOSE = args.verbose
subtypes = args.subtypes
from hivwholeseq.reference import load_custom_reference
from hivwholeseq.utils.sequence import find_annotation
ref = load_custom_reference('HXB2', 'gb')
for region in regions:
regm = np.array(find_annotation(ref, region).extract(ref), 'S1')
for subtype in subtypes:
fn = get_subtype_reference_alignment_filename(region,
subtype=subtype,
refname=refname,
VERBOSE=VERBOSE)
alim = np.array(AlignIO.read(fn, 'fasta'), 'S1')
weird = ((alim != regm).mean(axis=1) > 0.2)
print region, subtype, weird.sum()
parser.add_argument('--reference', required=True,
help='Reference to analyze (e.g. LAI-III)')
parser.add_argument('--fragments', nargs='+', default=fragments,
help='Fragments to merge')
parser.add_argument('--save', action='store_true',
help='Save to file')
parser.add_argument('--verbose', type=int, default=0,
help='Verbosity level [0-3]')
args = parser.parse_args()
refname = args.reference
use_save = args.save
VERBOSE = args.verbose
consensi = [load_custom_reference(refname+'_'+fr) for fr in fragments]
consensus = merge_sequences(consensi, VERBOSE=VERBOSE)
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
consrec = SeqRecord(Seq(consensus, alphabet=consensi[0].seq.alphabet),
id=refname+'_genomewide',
name=refname+'_genomewide',
description=refname+', genomewide reference (merged)',
)
if use_save:
if VERBOSE >= 1:
print 'Save to file'
from Bio import SeqIO
fn = get_custom_reference_filename(refname, format='fasta')
def make_reference(data_folder,
adaID,
fragments,
refname,
VERBOSE=0,
summary=True):
'''Make reference sequence trimmed to the necessary parts'''
from hivwholeseq.reference import load_custom_reference
seq = load_custom_reference(refname)
output_filename = get_reference_premap_filename(data_folder, adaID)
if fragments is None:
seq_trim = seq
else:
# Look for the first fwd and the last rev primers to trim the reference
# NOTE: this works even if F1 or F6 are missing (e.g. only F2-5 are seq-ed)!
# If more than one primer is used for the first or last fragment, take the
# longest reference
from hivwholeseq.data.primers import primers_PCR, primers_coordinates_HXB2
if '+' in fragments[0]:
fragment_subs = [
fragments[0][:2] + fsub + fragments[0][-1]
for fsub in fragments[0][2:-1].split('+')
]
fr_pos_subs = [
primers_coordinates_HXB2[fsub][0][0] for fsub in fragment_subs
]
fragments[0] = fragment_subs[np.argmin(fr_pos_subs)]
pr_fwd = primers_PCR[fragments[0]][0]
if '+' in fragments[-1]:
fragment_subs = [
fragments[-1][:2] + fsub + fragments[-1][-1]
for fsub in fragments[-1][2:-1].split('+')
]
fr_pos_subs = [
primers_coordinates_HXB2[fsub][1][1] for fsub in fragment_subs
]
fragments[-1] = fragment_subs[np.argmax(fr_pos_subs)]
pr_rev = primers_PCR[fragments[-1]][1]
smat = np.array(seq)
# Get all possible primers from ambiguous nucleotides and get the best match
from hivwholeseq.utils.sequence import expand_ambiguous_seq as eas
pr_fwd_mat = np.array(map(list, eas(pr_fwd)), 'S1')
n_matches_fwd = [
(smat[i:i + len(pr_fwd)] == pr_fwd_mat).sum(axis=1).max()
for i in xrange(len(seq) - len(pr_fwd))
]
pr_fwd_pos = np.argmax(n_matches_fwd)
pr_rev_mat = np.array(map(list, eas(pr_rev)), 'S1')
n_matches_rev = [
(smat[i:i + len(pr_rev)] == pr_rev_mat).sum(axis=1).max()
for i in xrange(pr_fwd_pos + len(pr_fwd),
len(seq) - len(pr_rev))
]
# Here you come from the right, i.e. look in the 3' LTR first
pr_rev_pos = len(seq) - len(pr_rev) - 1 - np.argmax(
n_matches_rev[::-1])
output = [['Reference name:', refname]]
output.append(['FWD primer:', fragments[0], str(pr_fwd_pos), pr_fwd])
output.append(['REV primer:', fragments[-1], str(pr_rev_pos), pr_rev])
output = '\n'.join(map(' '.join, output))
if VERBOSE:
print output
if summary:
with open(get_premap_summary_filename(data_folder, adaID),
'a') as f:
f.write(output)
f.write('\n')
# The reference includes both the first fwd primer and the last rev one
seq_trim = seq[pr_fwd_pos:pr_rev_pos + len(pr_rev)]
seq_trim.id = '_'.join(
[seq_trim.id,
str(pr_fwd_pos + 1),
str(pr_rev_pos + len(pr_rev))])
seq_trim.name = '_'.join([
seq_trim.name,
str(pr_fwd_pos + 1),
str(pr_rev_pos + len(pr_rev))
])
seq_trim.description = ' '.join([
seq_trim.description, 'from',
str(pr_fwd_pos + 1), 'to',
str(pr_rev_pos + len(pr_rev)),
'(indices from 1, extremes included)'
])
SeqIO.write(seq_trim, output_filename, 'fasta')
if summary:
with open(get_premap_summary_filename(data_folder, adaID), 'a') as f:
f.write('Reference sequence written to: ' + output_filename)
f.write('\n')
from hivwholeseq.utils.genome_info import all_edges, find_region_edges
edges_chunk = all_edges[name]
edges = find_region_edges(smat, edges_chunk)
# Some features must be stripped of primers
if name in ['V3']:
edges[0] += len(edges_chunk[0])
edges[1] -= len(edges_chunk[1])
return [edges]
# Script
if __name__ == '__main__':
seqold = load_custom_reference('HXB2', 'gb')
seqnew = load_custom_reference('HXB2', 'fasta')
smat = np.array(seqnew)
print 'Add features'
for typ, coord_typ in coordinates.iteritems():
for name, edges in coord_typ.iteritems():
# If coordinates are missing, grab primers
if edges is None:
edges = get_coordinates_feature(smat, name)
if len(edges) == 1:
fea = SeqFeature(FeatureLocation(edges[0][0],
edges[0][1],
strand=+1),
def correlate_epitope_substitution(ds, dctl):
'''Correlate presence of a substitution with epitope'''
from hivwholeseq.data.primers import primers_coordinates_HXB2_outer
start_F1 = primers_coordinates_HXB2_outer['F1'][0][1]
end_F6 = primers_coordinates_HXB2_outer['F6'][1][0]
ds = ds.copy()
dg = []
for pcode, datum in dctl.groupby('pcode'):
a = np.arange(start_F1, end_F6)
b = np.zeros(len(a), bool)
for _, epi in datum.iterrows():
b[(a >= epi['start_HXB2']) & (a < epi['end_HXB2'])] = True
c = np.zeros(len(a), bool)
datum = ds.loc[ds['pcode'] == pcode]
# Keep only nonsyn substitutions
datum = datum.loc[datum['syn'] == False]
c[datum['pos_ref'] - a[0]] = True
dat = {
'pos': a,
'epitope': b,
'substitution': c,
}
dat = pd.DataFrame(dat)
dat['pcode'] = pcode
dg.append(dat)
dg = pd.concat(dg)
# Exclude env because it has antibody-related substitutions
from hivwholeseq.reference import load_custom_reference
from hivwholeseq.utils.sequence import find_annotation
ref = load_custom_reference('HXB2', 'gb')
start_env = find_annotation(ref, 'gp41').location.nofuzzy_start
end_env = find_annotation(ref, 'gp41').location.nofuzzy_end - 450
dg = dg.loc[(dg['pos'] < start_env) | (dg['pos'] >= end_env)]
M = dg.groupby(['epitope', 'substitution']).size().unstack()
Ma = np.array(M)
xp = 1.0 * Ma[1, 0] / Ma[0, 0] * Ma[0, 1]
xs = Ma[1, 1] - xp
print M
from scipy.stats import fisher_exact
print 'Fisher\'s exact enrichment:', fisher_exact(Ma)[0]
print 'Fisher\'s exact P value:', fisher_exact(Ma)[1]
print 'expected:', xp
print 'excess:', xs, 'per patient:', xs / 9.0
pos_epi = dg.loc[dg['epitope'] == True]['pos'].unique()
dg2 = dg.loc[dg['pos'].isin(pos_epi)].copy()
M2 = dg2.groupby(['epitope', 'substitution']).size().unstack()
M2a = np.array(M2)
xp = 1.0 * M2a[1, 0] / M2a[0, 0] * M2a[0, 1]
xs = M2a[1, 1] - xp
print M2
print '\nFisher\'s exact enrichment:', fisher_exact(M2a)[0]
print 'Fisher\'s exact P value:', fisher_exact(M2a)[1]
print 'expected:', xp
print 'excess:', xs, 'per patient:', xs / 9.0
return {
'dg': dg,
'dg2': dg2,
}
parser.add_argument('--reference', default='HXB2',
help='Reference to use for alignment')
parser.add_argument('--verbose', type=int, default=0,
help='Verbosity level [0-4]')
parser.add_argument('--subtypes', nargs='+', default=['B'],
help='Subtypes to keep')
args = parser.parse_args()
regions = args.regions
refname = args.reference
VERBOSE = args.verbose
subtypes = args.subtypes
from hivwholeseq.reference import load_custom_reference
from hivwholeseq.utils.sequence import find_annotation
ref = load_custom_reference('HXB2', 'gb')
for region in regions:
regm = np.array(find_annotation(ref, region).extract(ref), 'S1')
for subtype in subtypes:
fn = get_subtype_reference_alignment_filename(region,
subtype=subtype,
refname=refname,
VERBOSE=VERBOSE)
alim = np.array(AlignIO.read(fn, 'fasta'), 'S1')
weird = ((alim != regm).mean(axis=1) > 0.2)
print region, subtype, weird.sum()
def make_reference(data_folder,
adaID,
fragments,
refname,
VERBOSE=0,
summary=True):
'''Make reference sequence trimmed to the necessary parts'''
from hivwholeseq.reference import load_custom_reference
seq = load_custom_reference(refname)
output_filename = get_reference_premap_filename(data_folder, adaID)
if fragments is None:
seq_trim = seq
else:
# Look for the first fwd and the last rev primers to trim the reference
# NOTE: this works even if F1 or F6 are missing (e.g. only F2-5 are seq-ed)!
# If more than one primer is used for the first or last fragment, take the
# longest reference
from hivwholeseq.data.primers import primers_PCR, primers_coordinates_HXB2
if '+' in fragments[0]:
fragment_subs = [
fragments[0][:2] + fsub + fragments[0][-1]
for fsub in fragments[0][2:-1].split('+')
]
fr_pos_subs = [
primers_coordinates_HXB2[fsub][0][0] for fsub in fragment_subs
]
fragments[0] = fragment_subs[np.argmin(fr_pos_subs)]
pr_fwd = primers_PCR[fragments[0]][0]
if '+' in fragments[-1]:
fragment_subs = [
fragments[-1][:2] + fsub + fragments[-1][-1]
for fsub in fragments[-1][2:-1].split('+')
]
fr_pos_subs = [
primers_coordinates_HXB2[fsub][1][1] for fsub in fragment_subs
]
fragments[-1] = fragment_subs[np.argmax(fr_pos_subs)]
pr_rev = primers_PCR[fragments[-1]][1]
smat = np.array(seq)
# Get all possible primers from ambiguous nucleotides and get the best match
from hivwholeseq.utils.sequence import expand_ambiguous_seq as eas
pr_fwd_mat = np.array(map(list, eas(pr_fwd)), 'S1')
n_matches_fwd = [
(smat[i:i + len(pr_fwd)] == pr_fwd_mat).sum(axis=1).max()
for i in xrange(len(seq) - len(pr_fwd))
]
pr_fwd_pos = np.argmax(n_matches_fwd)
pr_rev_mat = np.array(map(list, eas(pr_rev)), 'S1')
n_matches_rev = [
(smat[i:i + len(pr_rev)] == pr_rev_mat).sum(axis=1).max()
for i in xrange(pr_fwd_pos + len(pr_fwd),
len(seq) - len(pr_rev))
]
# Here you come from the right, i.e. look in the 3' LTR first
pr_rev_pos = len(seq) - len(pr_rev) - 1 - np.argmax(
n_matches_rev[::-1])
output = [['Reference name:', refname]]
output.append(['FWD primer:', fragments[0], str(pr_fwd_pos), pr_fwd])
output.append(['REV primer:', fragments[-1], str(pr_rev_pos), pr_rev])
output = '\n'.join(map(' '.join, output))
if VERBOSE:
print output
if summary:
with open(get_premap_summary_filename(data_folder, adaID),
'a') as f:
f.write(output)
f.write('\n')
# The reference includes both the first fwd primer and the last rev one
seq_trim = seq[pr_fwd_pos:pr_rev_pos + len(pr_rev)]
seq_trim.id = '_'.join(
[seq_trim.id,
str(pr_fwd_pos + 1),
str(pr_rev_pos + len(pr_rev))])
seq_trim.name = '_'.join([
seq_trim.name,
str(pr_fwd_pos + 1),
str(pr_rev_pos + len(pr_rev))
])
seq_trim.description = ' '.join([
seq_trim.description, 'from',
str(pr_fwd_pos + 1), 'to',
str(pr_rev_pos + len(pr_rev)),
'(indices from 1, extremes included)'
])
SeqIO.write(seq_trim, output_filename, 'fasta')
if summary:
with open(get_premap_summary_filename(data_folder, adaID), 'a') as f:
f.write('Reference sequence written to: ' + output_filename)
f.write('\n')
parser.add_argument('--fragments',
nargs='+',
default=fragments,
help='Fragments to merge')
parser.add_argument('--save', action='store_true', help='Save to file')
parser.add_argument('--verbose',
type=int,
default=0,
help='Verbosity level [0-3]')
args = parser.parse_args()
refname = args.reference
use_save = args.save
VERBOSE = args.verbose
consensi = [load_custom_reference(refname + '_' + fr) for fr in fragments]
consensus = merge_sequences(consensi, VERBOSE=VERBOSE)
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
consrec = SeqRecord(
Seq(consensus, alphabet=consensi[0].seq.alphabet),
id=refname + '_genomewide',
name=refname + '_genomewide',
description=refname + ', genomewide reference (merged)',
)
if use_save:
if VERBOSE >= 1:
print 'Save to file'
from Bio import SeqIO