def generatePoaGraph(self):
"""
Given list of subreads, generate MSA using POA graphs
"""
subreads = list(self.subreads)
if self.reference:
# the seeded sequence is the reference
root_subread = self.reference
self.root_subread = root_subread
root_seq = str(root_subread.sequence)
root_label = root_subread.header
else:
# no reference provided, align against subread
root_subread = subreads.pop()
self.root_subread = root_subread
root_seq = root_subread.read(aligned=False)
root_label = root_subread.qName
graph = poagraph.POAGraph(root_seq, label=root_label)
for subread in subreads:
# uses levenshtein distance to determine if sequence should
# be reverse-complemented before being added to the POA MSA
subread_seq = self._check_direction(subread.read(aligned=False),
root_seq)
subread_label = subread.qName
alignment = seqgraphalignment.SeqGraphAlignment(subread_seq,
graph,
fastMethod=True,
globalAlign=True)
graph.incorporateSeqAlignment(alignment,
subread_seq,
label=subread_label)
return graph
def test_equal_strings(fast, glob, n_sequences):
sequence = "TATACCGGCG"
sequences = [sequence]*n_sequences
graph = poagraph.POAGraph(sequences[0], "0")
for i in range(1, len(sequences)):
alignment = seqgraphalignment.SeqGraphAlignment(sequences[i], graph,
fastMethod=fast,
globalAlign=glob,
matchscore=1,
mismatchscore=-1,
gapscore=-2)
graph.incorporateSeqAlignment(alignment, sequence, str(i))
alignments = graph.generateAlignmentStrings()
matches = [alignments[0][1] == alignstr for _, alignstr in alignments]
assert all(matches)
def generate_poa_graph(sequences):
"""
Initialize graph and align all sequences
:param sequences: sequences to align
:return: graph: the completed POA graph resulting from the given sequences
"""
init_sequence = sequences[0]
init_label = "0"
graph = poagraph.POAGraph(init_sequence, init_label)
for i in range(1, len(sequences)):
sequence = sequences[i]
alignment = seqgraphalignment.SeqGraphAlignment(sequence,
graph,
fastMethod=False,
globalAlign=True,
matchscore=1,
mismatchscore=-1,
gapscore=-2)
graph.incorporateSeqAlignment(alignment, sequence, str(i))
return graph
def generatePoaGraph(self):
"""
Given list of subreads, generate MSA using POA graphs
"""
subreads = list(self.subreads)
root_subread = subreads.pop()
self.root_subread = root_subread
root_seq = root_subread.read(aligned=False)
root_label = root_subread.qName
graph = poagraph.POAGraph(root_seq, label=root_label)
for subread in subreads:
subread_seq = self._check_direction(subread.read(aligned=False),
root_seq)
subread_label = subread.qName
alignment = seqgraphalignment.SeqGraphAlignment(subread_seq,
graph,
fastMethod=True,
globalAlign=True)
graph.incorporateSeqAlignment(alignment,
subread_seq,
label=subread_label)
return graph
#!/usr/bin/env python
from __future__ import print_function
import argparse
import sys
import numpy
import poagraph
import seqgraphalignment
import simplefasta
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('infile', nargs='?', type=argparse.FileType('r'), default=sys.stdin)
parser.add_argument('-g','--globalAlign', action='store_true', help='Global alignment, or (default) local alignment')
args = parser.parse_args()
seqNo = 0
fasta = simplefasta.readfasta(args.infile)
graph = poagraph.POAGraph(fasta[0][1], fasta[0][0])
for label, sequence in fasta[1:]:
alignment = seqgraphalignment.SeqGraphAlignment(sequence, graph, globalAlign=args.globalAlign)
graph.incorporateSeqAlignment(alignment, sequence, label)
alignments = graph.generateAlignmentStrings()
for label,alignstring in alignments:
print("{0:15s} {1:s}".format(label, alignstring))
def InfoShield_MDL(pads, output_path):
init_cost = prev_total_cost = np.sum(
[sequence_cost(s) for _, s in pads.items()]) + len(pads)
gid_arr = np.array([l for l, _ in pads.items()])
start0 = time.time()
temp_arr, cond_arr, temp_dict, iter = [], [], {}, 0
while len(gid_arr) > 0:
iter += 1
graph, gid = poagraph.POAGraph(pads[gid_arr[0]], gid_arr[0]), [0]
seq_total_cost = sequence_cost(pads[gid_arr[0]])
graph_0 = copy.deepcopy(graph)
start1 = time.time()
for idx, label in enumerate(gid_arr[1:]):
sequence = pads[label]
alignment = seqgraphalignment.SeqGraphAlignment(sequence, graph_0)
align_mdl, _ = alignment.alignment_encoding_cost()
seq_cost = sequence_cost(sequence)
if align_mdl < seq_cost:
gid.append(idx + 1)
alignment = seqgraphalignment.SeqGraphAlignment(
sequence, graph)
graph.incorporateSeqAlignment(alignment, sequence, label)
seq_total_cost += seq_cost
end1 = time.time()
if len(gid) > 1:
template, min_cost = dichotomous_search(pads, gid_arr, gid, graph)
template = slot_identify(pads, gid_arr, gid, template)
align_cost, c_arr = 0, []
for id in gid:
sequence = pads[gid_arr[id]]
alignment = seqgraphalignment.SeqGraphAlignment(
sequence, template)
cost, cond = alignment.alignment_encoding_cost()
align_cost += cost
c_arr.append(cond)
total_cost = prev_total_cost - seq_total_cost
if len(temp_arr) != 0:
total_cost -= log_star(len(temp_arr)) + len(gid_arr) * ceil(
np.log2(len(temp_arr)))
total_cost += (len(gid_arr) + len(gid)) * ceil(
np.log2(len(temp_arr) + 1))
total_cost += log_star(len(temp_arr) +
1) + template.encoding_cost() + align_cost
### Check whether total cost decreases by this template
if total_cost < prev_total_cost:
prev_total_cost = total_cost
temp_arr.append(template)
cond_arr.append(c_arr)
temp_dict[len(temp_arr)] = gid_arr[gid]
### Delete the assigned sequences
gid_arr = np.delete(gid_arr, gid)
end0 = time.time()
output_results(temp_arr, cond_arr, output_path)
return (init_cost - prev_total_cost) / init_cost, temp_dict, end0 - start0