def main():
parser = argparse.ArgumentParser()
parser.add_argument("--smc", required=True, dest="smc", type=str, help="SMC file for the region of interest")
parser.add_argument("--pos", required=True, dest="pos", type=int, help="Position of STR in region")
parser.add_argument("--mu", required=True, dest="mu", type=float, help="Mutation rate for mutation model")
parser.add_argument("--beta", required=True, dest="beta", type=float, help="Length constraint for mutation model")
parser.add_argument("--pgeom", required=True, dest="pgeom", type=float, help="Geometric parameter for mutation model")
parser.add_argument("--out", required=True, dest="out", type=str, help="Output path prefix for phased VCF (+ _strs.vcf)")
parser.add_argument("--vcf", required=True, dest="vcf", type=str, help="Input path for VCF containing haploid STR calls")
parser.add_argument("--samps", required=False, dest="samps", type=str, help="File containing list of samples to consider")
parser.add_argument("--thresh", required=False, dest="thresh", type=float, help="Posterior probability threshold required to report phasing", default=0.5)
# Scaling factor from edge length to # of generations
parser.add_argument("--gen_per_len", required=False, dest="gen_per_len", type=float, default=1.0)
# Maximum TMRCA in generations
parser.add_argument("--max_tmrca", required=False, dest="max_tmrca", type=int, default=25000)
args = parser.parse_args()
tree, leaf_names, leaf_indices = extract_newick_tree_from_smc(args.smc, args.pos)
samples = read_sample_list(args.samps) if args.samps is not None else None
# Read haploid STR genotypes
nrepeats_dict, median_allele = read_haploid_str_gts(args.vcf, sample_set=samples)
# Ensure that all of the sample nodes are contained within the tree
for key in nrepeats_dict:
if key not in leaf_indices:
exit("ERROR: Sample %s not present in provided tree"%(key))
# Randomly pair haploid to construct pseudodiploids (node_id_1, node_id_2, num_repeat_a, num_repeat_b)
pair_data = pair_gts(nrepeats_dict, leaf_indices, pairs=None)
# Only deal with tree for diploid individuals
if len(tree.leaf_nodes()) % 2 != 0:
exit("ERROR: Tree contains an odd number of leaves")
# Construct the mutation model
print("Constructing the mutation model")
allele_range, max_step = determine_allele_range(args.max_tmrca, args.mu, args.beta, args.pgeom, 0, 0)
min_allele = -allele_range - max_step
max_allele = allele_range + max_step
mut_model = OUGeomSTRMutationModel(args.pgeom, args.mu, args.beta, allele_range, max_step = max_step)
print("Min allele = %d, Max allele = %d"%(min_allele, max_allele))
# Ensure that the observed genotypes are within the allele range
if len(pair_data) != 0:
min_obs_allele = min(min(map(lambda x: x[2], pair_data)), min(map(lambda x: x[3], pair_data)))
max_obs_allele = max(max(map(lambda x: x[2], pair_data)), max(map(lambda x: x[3], pair_data)))
if min_obs_allele < min_allele or max_obs_allele > max_allele:
exit("ERROR: Observed allele not within mutation model's allele range: (%d, %d)"%(min_obs_allele, max_obs_allele))
# Precompute the transition probabilities
print("Calculating the transition probabilities")
optimizer = MATRIX_OPTIMIZER(mut_model.trans_matrix, mut_model.min_n)
optimizer.precompute_results()
# Write out fixed paired information
pairs_file = tempfile.mkstemp()[1]
output = open(pairs_file, "w")
for i in xrange(len(pair_data)):
output.write("%d\t%d\t%d\t%d\n"%(pair_data[i][0], pair_data[i][1], pair_data[i][2]-min_allele, pair_data[i][3]-min_allele))
output.close()
# Write out the factor graph file
graph_file = tempfile.mkstemp()[1]
write_factor_graph(tree, optimizer, pair_data, min_allele, max_allele, args.gen_per_len, graph_file)
# Run c++ package, parse results and remove temporary files
phase_cmd_path = os.path.dirname(os.path.realpath(__file__)) + "/str-imputer"
phase_cmd = [phase_cmd_path, graph_file, pairs_file]
proc = subprocess.Popen(phase_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout, stderr = proc.communicate()
# TO DO: Utilize stderr messages to ensure convergence
#print(proc.stderr.read().strip())
#res = proc.stdout.read().strip()
res = stdout.strip()
rm_cmd = ["rm", "-f", graph_file, pairs_file]
subprocess.call(rm_cmd)
# Assess accuracy, output the statistics and determine the new genotypes associated with each sample
phased_repeat_dict, accuracy_string = process_phasing_result(res, leaf_names, min_allele, min_confidence = args.thresh)
print(accuracy_string)
# Construct a new VCF containing the phased alleles
write_vcf(args.vcf, phased_repeat_dict, median_allele, args.out + "_strs.vcf")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--smc", required=True, dest="smc", type=str, help="SMC file for the region of interest")
parser.add_argument("--chrom", required=True, dest="chrom", type=str, help="STR's Chromosome")
parser.add_argument("--pos", required=True, dest="pos", type=int, help="Position of STR in region")
parser.add_argument("--mu", required=True, dest="mu", type=float, help="Mutation rate for mutation model")
parser.add_argument("--beta", required=True, dest="beta", type=float, help="Length constraint for mutation model")
parser.add_argument("--pgeom", required=True, dest="pgeom", type=float, help="Geometric parameter for mutation model")
parser.add_argument("--out", required=True, dest="out", type=str, help="Output path prefix for imputed/phased VCF")
parser.add_argument("--vcf", required=True, dest="vcf", type=str, help="VCF containing STR calls")
parser.add_argument("--samps", required=False, dest="samps", type=str, help="File containing list of samples to consider")
parser.add_argument("--thresh", required=False, dest="thresh", type=float, help="Posterior probability threshold required to report phasing", default=0.5)
parser.add_argument("--phase", required=False, dest="phase", action="store_true", help="Output phasing statistics", default=False)
parser.add_argument("--impute", required=False, dest="impute", action="store_true", help="Output imputation statisttics", default=False)
parser.add_argument("--diploid", required=False, dest="diploid", action="store_true", help="VCF contains diploid STR calls (instead of haploid calls)", default=False)
# Scaling factor from edge length to # of generations
parser.add_argument("--gen_per_len", required=False, dest="gen_per_len", type=float, default=1.0)
# Maximum TMRCA in generations
parser.add_argument("--max_tmrca", required=False, dest="max_tmrca", type=int, default=25000)
args = parser.parse_args()
if (not args.phase and not args.impute) or (args.phase and args.impute):
exit("ERROR: Exactly one of --phase or --impute must be specified. Exiting...")
tree, leaf_names, leaf_indices = extract_newick_tree_from_smc(args.smc, args.pos)
samples = read_sample_list(args.samps) if args.samps is not None else None
# Read STR genotypes
if args.diploid:
nrepeats_dict, median_allele = read_diploid_str_gts(args.vcf, args.chrom, args.pos, sample_set=samples)
else:
nrepeats_dict, median_allele = read_haploid_str_gts(args.vcf, args.chrom, args.pos, sample_set=samples)
# Ensure that all of the sample nodes are contained within the tree
for key in nrepeats_dict:
if key not in leaf_indices:
exit("ERROR: Sample %s not present in provided tree"%(key))
if args.diploid:
# Pair _1 and _2 nodes together
sample_names = list(set(map(lambda x: x.split("_")[0], nrepeats_dict.keys())))
pairs = map(lambda x: (x+"_1", x+"_2"), sample_names)
pair_data = pair_gts(nrepeats_dict, leaf_indices, pairs=pairs)
else:
# Randomly pair haploid to construct pseudodiploids (node_id_1, node_id_2, num_repeat_a, num_repeat_b)
pair_data = pair_gts(nrepeats_dict, leaf_indices, pairs=None)
# Only deal with tree for even number of chromosomes
if len(tree.leaf_nodes()) % 2 != 0:
exit("ERROR: Tree contains an odd number of leaves")
# Construct the mutation model
print("Constructing the mutation model")
allele_range, max_step = determine_allele_range(args.max_tmrca, args.mu, args.beta, args.pgeom, 0, 0)
min_allele = -allele_range - max_step
max_allele = allele_range + max_step
mut_model = OUGeomSTRMutationModel(args.pgeom, args.mu, args.beta, allele_range, max_step = max_step)
print("Min allele = %d, Max allele = %d"%(min_allele, max_allele))
# Ensure that the observed genotypes are within the allele range
if len(pair_data) != 0:
min_obs_allele = min(min(map(lambda x: x[2], pair_data)), min(map(lambda x: x[3], pair_data)))
max_obs_allele = max(max(map(lambda x: x[2], pair_data)), max(map(lambda x: x[3], pair_data)))
if min_obs_allele < min_allele or max_obs_allele > max_allele:
exit("ERROR: Observed allele not within mutation model's allele range: (%d, %d)"%(min_obs_allele, max_obs_allele))
# Precompute the transition probabilities
print("Calculating the transition probabilities")
optimizer = MATRIX_OPTIMIZER(mut_model.trans_matrix, mut_model.min_n)
optimizer.precompute_results()
# Write out paired information for known diploid GTs
pairs_file = tempfile.mkstemp()[1]
output = open(pairs_file, "w")
for i in xrange(len(pair_data)):
output.write("%d\t%d\t%d\t%d\n"%(pair_data[i][0], pair_data[i][1], pair_data[i][2]-min_allele, pair_data[i][3]-min_allele))
output.close()
# Write out ids for any leaves not included in the VCF (for potential imputation queries)
ids_file = tempfile.mkstemp()[1]
output = open(ids_file, "w")
for leaf_name,leaf_id in leaf_indices.items():
if leaf_name not in nrepeats_dict:
output.write("%d\n"%(leaf_id))
output.close()
# Write out the factor graph file
graph_file = tempfile.mkstemp()[1]
write_factor_graph(tree, optimizer, pair_data, min_allele, max_allele, args.gen_per_len, graph_file)
# Run c++ package, parse results and remove temporary files
# TO DO: Utilize stderr messages to ensure convergence
cmd_path = os.path.dirname(os.path.realpath(__file__)) + "/Phaser"
if args.phase:
cmd = [cmd_path, "--factor-graph", graph_file, "--pair-file", pairs_file]
elif args.impute:
cmd = [cmd_path, "--factor-graph", graph_file, "--id-file", ids_file]
print("Running message-passing tool")
proc = subprocess.Popen(cmd, stdout=subprocess.PIPE)#, stderr=subprocess.PIPE)
stdout, stderr = proc.communicate()
res = stdout.strip()
rm_cmd = ["rm", "-f", graph_file, pairs_file, ids_file]
subprocess.call(rm_cmd)
# Assess accuracy, output the statistics and determine the new genotypes associated with each sample
if args.phase:
phased_repeat_dict, accuracy_string = process_phasing_result(res, leaf_names, min_allele, min_confidence=args.thresh)
#print(accuracy_string)
# Construct a new VCF containing the phased alleles
if args.diploid:
write_diploid_vcf(args.vcf, args.chrom, args.pos, phased_repeat_dict, median_allele, args.out + "_phased_strs.vcf")
else:
write_haploid_vcf(args.vcf, args.chrom, args.pos, phased_repeat_dict, median_allele, args.out + "_phased_strs.vcf")
# Determine the most probable posterior genotype for each sample
elif args.impute:
imputed_repeat_dict,posterior_dict,dosage_dict = process_imputation_result(res, leaf_names, min_allele, max_allele)
if args.diploid:
write_diploid_vcf(args.vcf, args.chrom, args.pos, imputed_repeat_dict, median_allele, args.out + "_imputed_strs.vcf", posteriors=posterior_dict, dosages=dosage_dict)
else:
write_haploid_vcf(args.vcf, args.chrom, args.pos, imputed_repeat_dict, median_allele, args.out + "_imputed_strs.vcf", posteriors=posterior_dict, dosages=dosage_dict)
