def get_consensus(read_fn, init_ref, consensus_fn, consens_seq_name,
hp_correction = True,
min_iteration = 4,
max_num_reads = 150,
entropy_th = 0.65):
g = construct_aln_graph_from_fasta(read_fn, init_ref, max_num_reads = max_num_reads, remove_in_del = False)
s,c = g.generate_consensus()
with open(consensus_fn,"w") as f:
print >>f, ">"+consens_seq_name
print >>f, s.upper()
if min_iteration > 1:
for i in range(min_iteration-2):
g = construct_aln_graph_from_fasta(read_fn, consensus_fn, max_num_reads = max_num_reads, remove_in_del = False)
s,c = g.generate_consensus()
with open(consensus_fn,"w") as f:
print >>f, ">"+consens_seq_name
print >>f, s.upper()
if hp_correction:
g = construct_aln_graph_from_fasta(read_fn, consensus_fn, max_num_reads = max_num_reads, remove_in_del = False)
s = detect_missing(g, entropy_th = entropy_th)
with open(consensus_fn,"w") as f:
print >>f, ">"+consens_seq_name
print >>f, s.upper()
g = construct_aln_graph_from_fasta(read_fn, consensus_fn, max_num_reads = max_num_reads, remove_in_del = False)
s,c = g.generate_consensus()
s = mark_lower_case_base(g, entropy_th = entropy_th)
with open(consensus_fn,"w") as f:
print >>f, ">"+consens_seq_name
print >>f, s
def generate_haplotype_consensus(inpute_fasta_name, ref_fasta_name, prefix, consensus_name,
hpFix = True,
min_iteration = 4,
max_num_reads = 150,
entropy_th = 0.65):
normalize_fasta(inpute_fasta_name, ref_fasta_name, "%s_input.fa" % prefix)
get_consensus("%s_input.fa" % prefix,
ref_fasta_name,
"%s.fa" % prefix,
consensus_name,
hp_correction = False,
min_iteration = min_iteration,
max_num_reads = max_num_reads,
entropy_th = entropy_th)
g = construct_aln_graph_from_fasta("%s_input.fa" % prefix,
"%s.fa" % prefix,
ref_group=consensus_name,
max_num_reads = max_num_reads,
remove_in_del = False)
rv, hen = read_node_vector(g, entropy_th = 0.65)
cluster, cluster_vec = clustering_read(rv, hen, k_cluster = 2, random_seed = 42)
with open("%s.log" % prefix, "w") as logf:
print >>logf, len(rv)
for k in cluster:
print >>logf, cluster_vec[k], k, len(cluster[k])
for k in cluster:
for r in cluster[k]:
print >>logf, "".join(rv[r]), k, r
if len(cluster[0]) > 0:
get_subset_reads("%s_input.fa" % prefix, cluster, 0, "%s_h1_input.fa" % prefix)
#rid,s = best_template_by_blasr("%s_h1_input.fa" % prefix)
#print rid, len(s)
#with open("%s_h1_ref.fa" % prefix, "w") as f:
# print >>f ,">%s_h1_ref" % prefix
# print >>f, s
get_consensus("%s_h1_input.fa" % prefix,
"%s.fa" % prefix,
"%s_h1.fa" % prefix,
"%s_h1" % consensus_name,
hp_correction = hpFix,
min_iteration = min_iteration,
max_num_reads = max_num_reads)
if len(cluster[1]) > 0:
get_subset_reads("%s_input.fa" % prefix, cluster, 1, "%s_h2_input.fa" % prefix)
#rid,s = best_template_by_blasr("%s_h2_input.fa" % prefix)
#print rid, len(s)
#with open("%s_h2_ref.fa" % prefix, "w") as f:
# print >>f ,">%s_h2_ref" % prefix
# print >>f, s
get_consensus("%s_h2_input.fa" % prefix,
"%s.fa" % prefix,
"%s_h2.fa" % prefix,
"%s_h2" % consensus_name,
hp_correction = hpFix,
min_iteration = min_iteration,
max_num_reads = max_num_reads)
