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|
# run as ctest .
#
# or
#
# cd ../test ; env PYTHONPATH=../build python3 tests/realign.py ; cd ../build
# cd ../test ; env PYTHONPATH=../build pytest tests/realign.py ; cd ../build
import unittest
from pyvcflib import *
import json
class RealignTest(unittest.TestCase):
# Returns True or False.
def test1(self):
vcf = VariantCallFile()
vcf.openFile("../samples/10158243.vcf")
var = Variant(vcf)
vcf.getNextVariant(var)
self.assertEqual(var.name,"grch38#chr4")
self.assertEqual(var.ref,'ACCCCCACCCCCACC')
self.assertEqual(var.alt,['ACC', 'AC', 'ACCCCCACCCCCAC', 'ACCCCCACC', 'ACA'])
# sw = var.legacy_parsedAlternates(False,False,False,10.0,-9.0,15.0,6.66,0.0,"","",False,False)
def test_sw_wf_compare(self):
vcf = VariantCallFile()
vcf.openFile("../samples/10134514.vcf")
var = Variant(vcf)
vcf.getNextVariant(var)
self.assertEqual(var.name,"grch38#chr4_10083863-10181258.vcf:grch38#chr4")
self.assertEqual(var.ref,'GGAGAATCCCAATTGATGG')
self.assertEqual(var.alt,['GTAGCATCCCAAGTGATGT', 'GTAGAATCCCAATTGATGT', 'GGAGCATCCCAATTGATGG', 'GG'])
# sw = var.legacy_parsedAlternates(False,False,False,10.0,-9.0,15.0,6.66,0.0,"","",False,False)
# for key, value in sw.items():
# print(f'SW allele key: {key}: ')
# for a in value:
# print(f' {a.position}/{a.ref}/{a.alt} ')
# note wf ignores paramaters
def test_wfbug2(self):
vcf = VariantCallFile()
vcf.openFile("../samples/10134514.vcf")
var = WfaVariant(vcf)
vcf.getNextVariant(var)
self.assertEqual(var.name,"grch38#chr4_10083863-10181258.vcf:grch38#chr4")
self.assertEqual(var.ref,'GGAGAATCCCAATTGATGG')
self.assertEqual(var.alt,['GTAGCATCCCAAGTGATGT', 'GTAGAATCCCAATTGATGT', 'GGAGCATCCCAATTGATGG', 'GG'])
wfa_params = wavefront_aligner_attr_default
# string paramString = "0,19,39,3,81,1";
wfa_params.distance_metric = distance_meric_t.gap_affine_2p
wfa_params.affine2p_penalties.match = 0
wfa_params.affine2p_penalties.mismatch = 19
wfa_params.affine2p_penalties.gap_opening1 = 39
wfa_params.affine2p_penalties.gap_extension1 = 3
wfa_params.affine2p_penalties.gap_opening2 = 81
wfa_params.affine2p_penalties.gap_extension2 = 1
wfa_params.alignment_scope = alignment_scope_t.compute_alignment;
# A dict is returned of alleles with variants and is_reversed
wf = var.wfa_parsedAlternates(True,True,False,"","",wfa_params,True,64,1,True)
print(f'ref={var.ref}')
print(var.info)
for key1, value1 in wf.items():
print(f'WF2 allele key: {key1}: ')
for a in value1[0]:
print(f' {a.position}:{a.ref}/{a.alt} ')
# Run a few tests
self.assertEqual(len(wf),5)
gg0 = wf['GG'][0][0]
gg1 = wf['GG'][0][1]
self.assertEqual(gg0.alt,"GG")
self.assertEqual(gg1.alt,"G")
self.assertEqual(wf['GGAGAATCCCAATTGATGG'][0][0].alt,"GGAGAATCCCAATTGATGG")
# Collect unique alleles
info = var.info
self.assertEqual(info['AC'],['11', '7', '1', '3'])
unique = {}
a = None
for alt0, wfvalue in wf.items(): # wfvalue is a compound of bool is_rev and alleles
is_rev = wfvalue[1]
for wfmatch in wfvalue[0]:
ref = wfmatch.ref
aligned = wfmatch.alt
wfpos = wfmatch.position
wftag = f'{alt0}:{wfpos}:{ref}/{aligned}'
if var.ref == aligned:
alt_index = -1
AC = None
AF = None
AN = None
else:
alt_index = var.alt.index(alt0) # Raises a ValueError if there is no such item
AC = int(info['AC'][alt_index])
AF = float(info['AF'][alt_index])
AN = int(info['AN'][0])
relpos = wfpos - var.pos
unique[wftag] = {
'pos0': var.pos,
'ref0': var.ref,
'alt0': alt0,
'ref1': ref,
'algn': aligned,
'pos1': wfpos,
'altidx': alt_index, # zero based
'relpos': relpos,
'AC': AC,
'AF': AF,
'AN': AN,
'is_rev': is_rev}
# Did we get all?
self.assertEqual(len(unique.items()),18)
# Display
uniqsorted = sorted(unique.items(),key = lambda r: r[1]['pos1'])
print(json.dumps(uniqsorted,indent=4))
# Check if all alleles were used by counting 'altidx'
idxs = set(map(lambda k: unique[k]['altidx'],unique.keys()))
self.assertEqual(len(idxs),len(var.alt)+1)
# Collect sample genotypes
samples = var.samples
gts = []
for sname in var.sampleNames:
# print(name,samples[name])
gt = (samples[sname]['GT'])[0].split("|")
# print(gt)
gts.append(list(map(lambda item: int(item) if item.isdigit() else None,gt)))
print(gts)
# for each variant translate genotypes
for tag,aln in uniqsorted:
# print(tag)
idx1 = aln['altidx']+1
# print(idx1)
genotypes = []
for gt in gts:
# print(gt)
genotypes.append(list(map(lambda item: item,gt)))
# print(list(genotypes))
# Now we neet to plug in the new indices
for gt in genotypes:
for i,g in enumerate(gt):
if g == idx1:
gt[i] = 1 # only one genotype in play
else:
if g != None:
gt[i] = 0
# print(list(genotypes))
aln['samples'] = genotypes
gts = None
print(uniqsorted[10])
self.assertEqual(uniqsorted[10][1]['samples'],[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, None], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 1], [0, 0], [0, 0], [0, 0], [0]])
# We are now going to merge records using a dict. From
#
# 10134515:G/T AC=7
# 10134515:G/T AC=11
# 10134516:AGAATCCCAATTGATGG/ AC=3
# 10134518:A/C AC=1
# 10134518:A/C AC=11
# 10134526:T/G AC=11
# 10134532:G/T AC=7
# 10134532:G/T AC=11
# into
# 10134515:G/T AC=18
# 10134516:AGAATCCCAATTGATGG/ AC=3
# 10134518:A/C AC=12
# 10134526:T/G AC=11
# 10134532:G/T AC=18
variants = {} # store new hash
k = None
v = None
for k,v in uniqsorted:
ref = v['ref1']
aligned = v['algn']
if ref != aligned:
ntag = f"{v['pos1']}:{ref}/{aligned}"
print(f"{ntag} AC={v['AC']}")
if ntag in variants:
variants[ntag]['AC'] += v['AC']
# Check AN number is equal so we can compute AF by addition
self.assertEqual(variants[ntag]['AN'],v['AN'])
variants[ntag]['AF'] += v['AF']
# Merge genotypes if they come from different alleles
if v['altidx'] != variants[ntag]['altidx']:
for i,samplesi in enumerate(variants[ntag]['samples']):
result = samplesi.copy()
g2 = v['samples'][i]
for j,samplej in enumerate(g2):
if g2[j] and g2[j]>0:
result[j] = g2[j]
# print(i,samplesi,v['samples'][i],result)
else:
variants[ntag] = v
print("into")
for key,v in variants.items():
print(f"{key} AC={v['AC']}")
self.assertEqual(len(variants),5)
self.assertEqual(variants['10134532:G/T']['AC'],18)
# Adjust TYPE field to set snp/mnp/ins/del
key = None
v = None
for key,v in variants.items():
ref_len = len(v['ref1'])
aln_len = len(v['algn'])
type = None
size = None
if aln_len < ref_len:
type = 'del'
size = ref_len - aln_len
elif aln_len > ref_len:
type = 'ins'
size = aln_len - ref_len
elif aln_len == ref_len:
if ref_len == 1:
type = 'snp'
else:
type = 'mnp'
size = aln_len
assert(size > 0)
variants[key]['type'] = type
variants[key]['size'] = size
# Set origin
print(v)
variants[key]['origin'] = f'{var.name}:{var.pos}'
# print(json.dumps(variants,indent=4))
# handle deletions. If ref length is larger than the WF matched
# allele length make this a missing genotype for all individual
# SNP/MNP calls that match the allele index and fall inside the
# deletion (region).
#
# The idea is that when a deletion exists for a sample there is
# no way a SNP/MNP gets called in that sample. So for this deletion:
# {'pos0': 10134514, 'ref0': 'GGAGAATCCCAATTGATGG', 'alt0': 'GG', 'ref1': 'GAGAATCCCAATTGATGG', 'algn': 'G', 'pos1': 10134515, 'altidx': 3, 'relpos': 1, 'AC': 3, 'AF': 0.0340909, 'AN': 88, 'is_rev': False, 'samples': [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, None], [0, 0], [0, 0], [0, 0], [1, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [1, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [1, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0]], 'type': 'del'}
# run through all other alleles and see if the genotype calls overlap. If
# the SNP/MNP overlaps the deletion make it a NULL call. It means running
# through all variants for every deletion. After discussion with Erik we
# turn all haplotypes to NULL.
type = None
for key,v in variants.items():
if v['type'] == 'del':
# for every deletion
del_ref_len = len(v['ref1'])
del_aln_len = len(v['algn'])
# del_len = del_ref_len - del_aln_len
del_pos1 = v['pos1']
del_size = v['size']
del_start_pos = del_pos1 + del_aln_len
# Make a range from the start of the deletion to the end
check_range = range(del_start_pos, del_start_pos + del_size)
check_samples = v['samples']
for key2,v2 in variants.items():
if v2['type'] == 'snp' or v2['type'] == 'mnp':
# for alignment check all SNPs/MNPs
pos1 = v2['pos1']
pos2 = pos1 + v2['size']
if pos1 in check_range or pos2 in check_range:
# compare all genotypes
for i,sample in enumerate(v2['samples']):
del_sample = check_samples[i]
nullify = False
if 1 in del_sample and 1 in sample:
nullify = True
print(i,sample,del_sample,nullify)
if nullify:
# v2['samples'][i] = [None if item == 1 else item for item in sample]
v2['samples'][i] = [None for item in sample]
# Recompute AC and AF using the actual genotypes
print("WIP")
print(variants)
if __name__ == '__main__':
unittest.main()
|
