/usr/bin/falcon_asm is in falconkit 0.1.3+20140820-1.
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# with the distribution.
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# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
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# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE
# GRANTED BY THIS LICENSE. THIS SOFTWARE IS PROVIDED BY PACIFIC
# BIOSCIENCES AND ITS CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED
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from pbcore.io import FastaReader
import networkx as nx
import os
import shlex
import sys
import subprocess
DEBUG_LOG_LEVEL = 0
class SGNode(object):
"""
class representing a node in the string graph
"""
def __init__(self, node_name):
self.name = node_name
self.out_edges = []
self.in_edges = []
def add_out_edge(self, out_edge):
self.out_edges.append(out_edge)
def add_in_edge(self, in_edge):
self.in_edges.append(in_edge)
class SGEdge(object):
"""
class representing an edge in the string graph
"""
def __init__(self, in_node, out_node):
self.in_node = in_node
self.out_node = out_node
self.attr = {}
def set_attribute(self, attr, value):
self.attr[attr] = value
def reverse_end( node_id ):
node_id, end = node_id.split(":")
new_end = "B" if end == "E" else "E"
return node_id + ":" + new_end
class StringGraph(object):
"""
class representing the string graph
"""
def __init__(self):
self.nodes = {}
self.edges = {}
self.n_mark = {}
self.e_reduce = {}
self.repeat_overlap = {}
def add_node(self, node_name):
"""
add a node into the graph by given a node name
"""
if node_name not in self.nodes:
self.nodes[node_name] = SGNode(node_name)
def add_edge(self, in_node_name, out_node_name, **attributes):
"""
add an edge into the graph by given a pair of nodes
"""
if (in_node_name, out_node_name) not in self.edges:
self.add_node(in_node_name)
self.add_node(out_node_name)
in_node = self.nodes[in_node_name]
out_node = self.nodes[out_node_name]
edge = SGEdge(in_node, out_node)
self.edges[ (in_node_name, out_node_name) ] = edge
in_node.add_out_edge(edge)
out_node.add_in_edge(edge)
edge = self.edges[ (in_node_name, out_node_name) ]
for k, v in attributes.items():
edge.attr[k] = v
def init_reduce_dict(self):
for e in self.edges:
self.e_reduce[e] = False
def mark_chimer_edge(self):
for e_n, e in self.edges.items():
v = e_n[0]
w = e_n[1]
overlap_count = 0
for w_out_e in self.nodes[w].out_edges:
w_out_n = w_out_e.out_node.name
if (v, w_out_n) in self.edges:
overlap_count += 1
for v_in_e in self.nodes[v].in_edges:
v_in_n = v_in_e.in_node.name
if (v_in_n, w) in self.edges:
overlap_count += 1
if self.e_reduce[ (v, w) ] != True:
if overlap_count == 0:
self.e_reduce[(v, w)] = True
#print "XXX: chimer edge %s %s removed" % (v, w)
v, w = reverse_end(w), reverse_end(v)
self.e_reduce[(v, w)] = True
#print "XXX: chimer edge %s %s removed" % (v, w)
def mark_spur_edge(self):
for v in self.nodes:
if len(self.nodes[v].out_edges) > 1:
for out_edge in self.nodes[v].out_edges:
w = out_edge.out_node.name
if len(self.nodes[w].out_edges) == 0 and self.e_reduce[ (v, w) ] != True:
#print "XXX: spur edge %s %s removed" % (v, w)
self.e_reduce[(v, w)] = True
v2, w2 = reverse_end(w), reverse_end(v)
#print "XXX: spur edge %s %s removed" % (v2, w2)
self.e_reduce[(v, w)] = True
if len(self.nodes[v].in_edges) > 1:
for in_edge in self.nodes[v].in_edges:
w = in_edge.in_node.name
if len(self.nodes[w].in_edges) == 0 and self.e_reduce[ (w, v) ] != True:
#print "XXX: spur edge %s %s removed" % (w, v)
self.e_reduce[(w, v)] = True
v2, w2 = reverse_end(w), reverse_end(v)
#print "XXX: spur edge %s %s removed" % (w2, v2)
self.e_reduce[(w, v)] = True
def mark_tr_edges(self):
"""
transitive reduction
"""
n_mark = self.n_mark
e_reduce = self.e_reduce
FUZZ = 500
for n in self.nodes:
n_mark[n] = "vacant"
for n_name, node in self.nodes.items():
out_edges = node.out_edges
if len(out_edges) == 0:
continue
out_edges.sort(key=lambda x: x.attr["length"])
for e in out_edges:
w = e.out_node
n_mark[ w.name ] = "inplay"
max_len = out_edges[-1].attr["length"]
max_len += FUZZ
for e in out_edges:
e_len = e.attr["length"]
w = e.out_node
if n_mark[w.name] == "inplay":
w.out_edges.sort( key=lambda x: x.attr["length"] )
for e2 in w.out_edges:
if e2.attr["length"] + e_len < max_len:
x = e2.out_node
if n_mark[x.name] == "inplay":
n_mark[x.name] = "eliminated"
for e in out_edges:
e_len = e.attr["length"]
w = e.out_node
w.out_edges.sort( key=lambda x: x.attr["length"] )
if len(w.out_edges) > 0:
x = w.out_edges[0].out_node
if n_mark[x.name] == "inplay":
n_mark[x.name] = "eliminated"
for e2 in w.out_edges:
if e2.attr["length"] < FUZZ:
x = e2.out_node
if n_mark[x.name] == "inplay":
n_mark[x.name] = "eliminated"
for out_edge in out_edges:
v = out_edge.in_node
w = out_edge.out_node
if n_mark[w.name] == "eliminated":
e_reduce[ (v.name, w.name) ] = True
#print "XXX: tr edge %s %s removed" % (v.name, w.name)
v_name, w_name = reverse_end(w.name), reverse_end(v.name)
e_reduce[(v_name, w_name)] = True
#print "XXX: tr edge %s %s removed" % (v_name, w_name)
n_mark[w.name] = "vacant"
def mark_best_overlap(self):
"""
find the best overlapped edges
"""
best_edges = set()
for v in self.nodes:
out_edges = self.nodes[v].out_edges
if len(out_edges) > 0:
out_edges.sort(key=lambda e: e.attr["score"])
e = out_edges[-1]
best_edges.add( (e.in_node.name, e.out_node.name) )
in_edges = self.nodes[v].in_edges
if len(in_edges) > 0:
in_edges.sort(key=lambda e: e.attr["score"])
e = in_edges[-1]
best_edges.add( (e.in_node.name, e.out_node.name) )
if DEBUG_LOG_LEVEL > 1:
print "X", len(best_edges)
for e_n, e in self.edges.items():
v = e_n[0]
w = e_n[1]
if self.e_reduce[ (v, w) ] != True:
if (v, w) not in best_edges:
self.e_reduce[(v, w)] = True
#print "XXX: in best edge %s %s removed" % (v, w)
v2, w2 = reverse_end(w), reverse_end(v)
#print "XXX: in best edge %s %s removed" % (v2, w2)
self.e_reduce[(v2, w2)] = True
def get_out_edges_for_node(self, name, mask=True):
rtn = []
for e in self.nodes[name].out_edges:
v = e.in_node
w = e.out_node
if self.e_reduce[ (v.name, w.name) ] == False:
rtn.append(e)
return rtn
def get_in_edges_for_node(self, name, mask=True):
rtn = []
for e in self.nodes[name].in_edges:
v = e.in_node
w = e.out_node
if self.e_reduce[ (v.name, w.name) ] == False:
rtn.append(e)
return rtn
def get_best_out_edge_for_node(self, name, mask=True):
rtn = []
for e in self.nodes[name].out_edges:
v = e.in_node
w = e.out_node
if self.e_reduce[ (v.name, w.name) ] == False:
rtn.append(e)
rtn.sort(key=lambda e: e.attr["score"])
return rtn[-1]
def get_best_in_edge_for_node(self, name, mask=True):
rtn = []
for e in self.nodes[name].in_edges:
v = e.in_node
w = e.out_node
if self.e_reduce[ (v.name, w.name) ] == False:
rtn.append(e)
rtn.sort(key=lambda e: e.attr["score"])
return rtn[-1]
RCMAP = dict(zip("ACGTacgtNn-","TGCAtgcaNn-"))
def generate_seq_from_path(sg, seqs, path):
subseqs = []
r_id, end = path[0].split(":")
count = 0
for i in range( len( path ) -1 ):
w_n, v_n = path[i:i+2]
edge = sg.edges[ (w_n, v_n ) ]
read_id, coor = edge.attr["label"].split(":")
b,e = coor.split("-")
b = int(b)
e = int(e)
if b < e:
subseqs.append( seqs[read_id][b:e] )
else:
subseqs.append( "".join( [RCMAP[c] for c in seqs[read_id][b:e:-1]] ) )
return "".join(subseqs)
def reverse_path( path ):
new_path = []
for n in list(path[::-1]):
rid, end = n.split(":")
new_end = "B" if end == "E" else "E"
new_path.append( rid+":"+new_end)
return new_path
def generate_unitig(sg, seqs, out_fn, connected_nodes = None):
"""
given a string graph:sg and the sequences: seqs, write the unitig fasta file into out_fn
the funtion return a reduct graph representing the reduce string graph where the edges are unitigs
some extra files generated:
unit_edges.dat : an easy to parse file for unitig data
unit_edge_paths : the file contains the information of the path of all unitigs
uni_graph.gexf: the unitig graph in gexf format for visulization
"""
G = SGToNXG(sg)
if connected_nodes != None:
connected_nodes = set(sg.nodes)
out_fasta = open(out_fn, "w")
nodes_for_tig = set()
sg_edges = set()
for v, w in sg.edges:
if sg.e_reduce[(v, w)] != True:
sg_edges.add( (v, w) )
count = 0
edges_in_tigs = set()
uni_edges = {}
path_f = open("unit_edge_paths","w")
uni_edge_f = open("unit_edges.dat", "w")
while len(sg_edges) > 0:
v, w = sg_edges.pop()
#nodes_for_tig.remove(n)
upstream_nodes = []
c_node = v
p_in_edges = sg.get_in_edges_for_node(c_node)
p_out_edges = sg.get_out_edges_for_node(c_node)
while len(p_in_edges) == 1 and len(p_out_edges) == 1:
p_node = p_in_edges[0].in_node
upstream_nodes.append(p_node.name)
if (p_node.name, c_node) not in sg_edges:
break
p_in_edges = sg.get_in_edges_for_node(p_node.name)
p_out_edges = sg.get_out_edges_for_node(p_node.name)
c_node = p_node.name
upstream_nodes.reverse()
downstream_nodes = []
c_node = w
n_out_edges = sg.get_out_edges_for_node(c_node)
n_in_edges = sg.get_in_edges_for_node(c_node)
while len(n_out_edges) == 1 and len(n_in_edges) == 1:
n_node = n_out_edges[0].out_node
downstream_nodes.append(n_node.name)
if (c_node, n_node.name) not in sg_edges:
break
n_out_edges = sg.get_out_edges_for_node(n_node.name)
n_in_edges = sg.get_in_edges_for_node(n_node.name)
c_node = n_node.name
whole_path = upstream_nodes + [v, w] + downstream_nodes
count += 1
subseq = generate_seq_from_path(sg, seqs, whole_path)
uni_edges.setdefault( (whole_path[0], whole_path[-1]), [] )
uni_edges[(whole_path[0], whole_path[-1])].append( ( whole_path, subseq ) )
print >> uni_edge_f, whole_path[0], whole_path[-1], "-".join(whole_path), subseq
print >>path_f, ">%05dc-%s-%s-%d %s" % (count, whole_path[0], whole_path[-1], len(whole_path), " ".join(whole_path))
print >>out_fasta, ">%05dc-%s-%s-%d" % (count, whole_path[0], whole_path[-1], len(whole_path))
print >>out_fasta, subseq
for i in range( len( whole_path ) -1 ):
w_n, v_n = whole_path[i:i+2]
try:
sg_edges.remove( (w_n, v_n) )
except KeyError: #if an edge is already deleted, ignore it
pass
r_whole_path = reverse_path( whole_path )
count += 1
subseq = generate_seq_from_path(sg, seqs, r_whole_path)
uni_edges.setdefault( (r_whole_path[0], r_whole_path[-1]), [] )
uni_edges[(r_whole_path[0], r_whole_path[-1])].append( ( r_whole_path, subseq ) )
print >> uni_edge_f, r_whole_path[0], r_whole_path[-1], "-".join(r_whole_path), subseq
print >>path_f, ">%05dc-%s-%s-%d %s" % (count, r_whole_path[0], r_whole_path[-1], len(r_whole_path), " ".join(r_whole_path))
print >>out_fasta, ">%05dc-%s-%s-%d" % (count, r_whole_path[0], r_whole_path[-1], len(r_whole_path))
print >>out_fasta, subseq
for i in range( len( r_whole_path ) -1 ):
w_n, v_n = r_whole_path[i:i+2]
try:
sg_edges.remove( (w_n, v_n) )
except KeyError: #if an edge is already deleted, ignore it
pass
path_f.close()
uni_edge_f.close()
#uni_graph = nx.DiGraph()
#for n1, n2 in uni_edges.keys():
# uni_graph.add_edge(n1, n2, count = len( uni_edges[ (n1,n2) ] ))
#nx.write_gexf(uni_graph, "uni_graph.gexf")
out_fasta.close()
return uni_edges
def neighbor_bound(G, v, w, radius):
"""
test if the node v and the node w are connected within a radius in graph G
"""
g1 = nx.ego_graph(G, v, radius=radius, undirected=False)
g2 = nx.ego_graph(G, w, radius=radius, undirected=False)
if len(set(g1.edges()) & set(g2.edges())) > 0:
return True
else:
return False
def is_branch_node(G, n):
"""
test whether the node n is a "branch node" which the paths from any of two of
its offsprings do not intersect within a given radius
"""
out_edges = G.out_edges([n])
n2 = [ e[1] for e in out_edges ]
is_branch = False
for i in range(len(n2)):
for j in range(i+1, len(n2)):
v = n2[i]
w = n2[j]
if neighbor_bound(G, v, w, 10) == False:
is_branch = True
break
if is_branch == True:
break
return is_branch
def get_bundle( path, u_graph ):
"""
find a sub-graph contain the nodes between the start and the end of the path
inputs:
u_graph : a unitig graph
returns:
bundle_graph: the whole bundle graph
bundle_paths: the paths in the bundle graph
sub_graph2_edges: all edges of the bundle graph
"""
p_start, p_end = path[0], path[-1]
p_nodes = set(path)
p_edges = set(zip(path[:-1], path[1:]))
u_graph_r = u_graph.reverse()
down_path = nx.ego_graph(u_graph, p_start, radius=len(p_nodes), undirected=False)
up_path = nx.ego_graph(u_graph_r, p_end, radius=len(p_nodes), undirected=False)
subgraph_nodes = set(down_path) & set(up_path)
sub_graph = nx.DiGraph()
for v, w in u_graph.edges_iter():
if v in subgraph_nodes and w in subgraph_nodes:
if (v, w) in p_edges:
sub_graph.add_edge(v, w, color = "red")
else:
sub_graph.add_edge(v, w, color = "black")
sub_graph2 = nx.DiGraph()
tips = set()
tips.add(path[0])
sub_graph_r = sub_graph.reverse()
visited = set()
ct = 0
is_branch = is_branch_node(sub_graph, path[0]) #if the start node is a branch node
if is_branch:
n = tips.pop()
e = sub_graph.out_edges([n])[0] #pick one path the build the subgraph
sub_graph2.add_edge(e[0], e[1], n_weight = u_graph[e[0]][e[1]]["n_weight"])
if e[1] not in visited:
last_node = e[1]
visited.add(e[1])
r_id, orientation = e[1].split(":")
orientation = "E" if orientation == "B" else "E"
visited.add( r_id +":" + orientation)
if not is_branch_node(sub_graph_r, e[1]):
tips.add(e[1])
while len(tips) != 0:
n = tips.pop()
out_edges = sub_graph.out_edges([n])
if len(out_edges) == 1:
e = out_edges[0]
sub_graph2.add_edge(e[0], e[1], n_weight = u_graph[e[0]][e[1]]["n_weight"])
last_node = e[1]
if e[1] not in visited:
visited.add(e[1])
r_id, orientation = e[1].split(":")
orientation = "E" if orientation == "B" else "E"
visited.add( r_id +":" + orientation)
if not is_branch_node(sub_graph_r, e[1]):
tips.add(e[1])
else:
is_branch = is_branch_node(sub_graph, n)
if not is_branch:
for e in out_edges:
sub_graph2.add_edge(e[0], e[1], n_weight = u_graph[e[0]][e[1]]["n_weight"])
last_node = e[1]
if e[1] not in visited:
r_id, orientation = e[1].split(":")
visited.add(e[1])
orientation = "E" if orientation == "B" else "E"
visited.add( r_id +":" + orientation)
if not is_branch_node(sub_graph_r, e[1]):
tips.add(e[1])
ct += 1
last_node = None
longest_len = 0
sub_graph2_nodes = sub_graph2.nodes()
sub_graph2_edges = sub_graph2.edges()
new_path = [path[0]]
for n in sub_graph2_nodes:
if len(sub_graph2.out_edges(n)) == 0 :
path_t = nx.shortest_path(sub_graph2, source = path[0], target = n, weight = "n_weight")
path_len = len(path_t)
if path_len > longest_len:
last_node = n
longest_len = path_len
new_path = path_t
if last_node == None:
for n in sub_graph2_nodes:
path_t = nx.shortest_path(sub_graph2, source = path[0], target = n, weight = "n_weight")
path_len = len(path_t)
if path_len > longest_len:
last_node = n
longest_len = path_len
new_path = path_t
path = new_path
# clean up sub_graph2 according to new begin and end
sub_graph2_r = sub_graph2.reverse()
down_path = nx.ego_graph(sub_graph2, path[0], radius=len(path), undirected=False)
up_path = nx.ego_graph(sub_graph2_r, path[-1], radius=len(path), undirected=False)
subgraph_nodes = set(down_path) & set(up_path)
for v in sub_graph2_nodes:
if v not in subgraph_nodes:
sub_graph2.remove_node(v)
if DEBUG_LOG_LEVEL > 1:
print "new_path", path[0], last_node, len(sub_graph2_nodes), path
bundle_paths = [path]
p_nodes = set(path)
p_edges = set(zip(path[:-1], path[1:]))
sub_graph2_nodes = sub_graph2.nodes()
sub_graph2_edges = sub_graph2.edges()
nodes_idx = dict( [ (n[1], n[0]) for n in enumerate(path) ] )
# create a list of subpath that has no branch
non_branch_subpaths = []
wi = 0
vi = 0
v = path[0]
while v != path[-1] and wi < len(path)-1:
wi += 1
w = path[wi]
while len( sub_graph2.successors(w) ) == 1 and len( sub_graph2.predecessors(w) ) == 1 and wi < len(path)-1:
wi += 1
w = path[wi]
if len( sub_graph2.successors(v) )!= 1 or len( sub_graph2.predecessors(w) )!= 1:
branched = True
else:
branched = False
if not branched:
non_branch_subpaths.append( path[vi:wi+1] )
v = w
vi = wi
# create the accompany_graph that has the path of the alternative subpaths
associate_graph = nx.DiGraph()
for v, w in sub_graph2.edges_iter():
if (v, w) not in p_edges:
associate_graph.add_edge(v, w, n_weight = sub_graph2[v][w]["n_weight"])
if DEBUG_LOG_LEVEL > 1:
print "associate_graph size:", len(associate_graph)
print "non_branch_subpaths",len(non_branch_subpaths), non_branch_subpaths
# construct the bundle graph
associate_graph_nodes = set(associate_graph.nodes())
bundle_graph = nx.DiGraph()
bundle_graph.add_path( path )
for i in range(len(non_branch_subpaths)-1):
if len(non_branch_subpaths[i]) == 0 or len( non_branch_subpaths[i+1] ) == 0:
continue
e1, e2 = non_branch_subpaths[i: i+2]
v = e1[-1]
w = e2[0]
if v == w:
continue
in_between_node_count = nodes_idx[w] - nodes_idx[v]
if v in associate_graph_nodes and w in associate_graph_nodes:
try:
a_path = nx.shortest_path(associate_graph, v, w, "n_weight")
except nx.NetworkXNoPath:
continue
bundle_graph.add_path( a_path )
bundle_paths.append( a_path )
return bundle_graph, bundle_paths, sub_graph2_edges
def get_bundles(u_edges):
"""
input: all unitig edges
output: the assembled primary_tigs.fa and all_tigs.fa
"""
ASM_graph = nx.DiGraph()
out_f = open("primary_tigs.fa", "w")
main_tig_paths = open("primary_tigs_paths","w")
sv_tigs = open("all_tigs.fa","w")
sv_tig_paths = open("all_tigs_paths","w")
max_weight = 0
for v, w in u_edges:
x = max( [len(s[1]) for s in u_edges[ (v,w) ] ] )
if DEBUG_LOG_LEVEL > 1:
print "W", v, w, x
if x > max_weight:
max_weight = x
in_edges = {}
out_edges = {}
for v, w in u_edges:
in_edges.setdefault(w, [])
out_edges.setdefault(w, [])
in_edges[w].append( (v, w) )
out_edges.setdefault(v, [])
in_edges.setdefault(v, [])
out_edges[v].append( (v, w) )
u_graph = nx.DiGraph()
for v,w in u_edges:
u_graph.add_edge(v, w, n_weight = max_weight - max( [len(s[1]) for s in u_edges[ (v,w) ] ] ) )
bundle_edge_out = open("bundle_edges","w")
bundle_index = 0
G = u_graph.copy()
visited_u_edges = set()
while len(G) > 0:
root_nodes = set()
for n in G:
if G.in_degree(n) == 0:
root_nodes.add(n)
if len(root_nodes) == 0:
if G.in_degree(n) != 1:
root_nodes.add(n)
if len(root_nodes) == 0:
root_nodes.add( G.nodes()[0] )
candidates = []
for n in list(root_nodes):
sp =nx.single_source_shortest_path_length(G, n)
sp = sp.items()
sp.sort(key=lambda x : x[1])
longest = sp[-1]
if DEBUG_LOG_LEVEL > 2:
print "L", n, longest[0]
if longest[0].split(":")[0] == n.split(":")[0]: #avoid a big loop
continue
candidates.append ( (longest[1], n, longest[0]) )
if len(candidates) == 0:
print "no more candiate", len(G.edges()), len(G.nodes())
if len(G.edges()) > 0:
path = G.edges()[0]
print path
else:
break
else:
candidates.sort()
candidate = candidates[-1]
if candidate[1] == candidate[2]:
G.remove_node(candidate[1])
continue
path = nx.shortest_path(G, candidate[1], candidate[2], "n_weight")
if DEBUG_LOG_LEVEL > 1:
print "X", path[0], path[-1], len(path)
cmp_edges = set()
g_edges = set(G.edges())
new_path = []
tail = True
# avioid confusion due to long palindrome sequence
if len(path) > 2:
for i in range( 0, len( path ) - 1 ):
v_n, w_n = path[i:i+2]
new_path.append(v_n)
# the comment out code below might be useful for filter out some high connectivity nodes
#if (v_n, w_n) in cmp_edges or\
# len(u_graph.out_edges(w_n)) > 5 or\
# len(u_graph.in_edges(w_n)) > 5:
if (v_n, w_n) in cmp_edges:
tail = False
break
r_id, end = v_n.split(":")
end = "E" if end == "B" else "B"
v_n2 = r_id + ":" + end
r_id, end = w_n.split(":")
end = "E" if end == "B" else "B"
w_n2 = r_id + ":" + end
if (w_n2, v_n2) in g_edges:
cmp_edges.add( (w_n2, v_n2) )
if tail:
new_path.append(w_n)
else:
new_path = path[:]
if len(new_path) > 1:
path = new_path
if DEBUG_LOG_LEVEL > 2:
print "Y", path[0], path[-1], len(path)
bundle_graph, bundle_paths, bundle_graph_edges = get_bundle( path, G )
for bg_edge in bundle_graph_edges:
print >> bundle_edge_out, bundle_index, "edge", bg_edge[0], bg_edge[1]
for path_ in bundle_paths:
print >>bundle_edge_out, "path", bundle_index, " ".join(path_)
edges_to_be_removed = set()
if DEBUG_LOG_LEVEL > 2:
print "Z", bundle_paths[0][0], bundle_paths[0][-1]
print bundle_index, len(path), len(bundle_paths[0]), len(bundle_paths), len(bundle_graph_edges)
if len(bundle_graph_edges) > 0:
ASM_graph.add_path(bundle_paths[0], ctg="%04d" % bundle_index)
extra_u_edges = []
print >> main_tig_paths, ">%04d %s" % ( bundle_index, " ".join(bundle_paths[0]) )
subseqs = []
for i in range(len(bundle_paths[0]) - 1):
v, w = bundle_paths[0][i:i+2]
edges_to_be_removed.add( (v,w) )
uedges = u_edges[ (v,w) ]
uedges.sort( key= lambda x: len(x[0]) )
subseqs.append( uedges[-1][1] )
visited_u_edges.add( "-".join(uedges[-1][0]) )
for ue in uedges:
if "-".join(ue[0]) not in visited_u_edges:
visited_u_edges.add("-".join(ue[0]))
extra_u_edges.append(ue)
seq = "".join(subseqs)
sv_tig_idx = 0
print >> sv_tig_paths, ">%04d-%04d %s" % ( bundle_index, sv_tig_idx, " ".join(bundle_paths[0]) )
if len(seq) > 0:
print >> out_f, ">%04d %s-%s" % (bundle_index, bundle_paths[0][0], bundle_paths[0][-1])
print >> out_f, seq
print >> sv_tigs, ">%04d-%04d %s-%s" % (bundle_index, sv_tig_idx, bundle_paths[0][0], bundle_paths[0][-1])
print >> sv_tigs, "".join(subseqs)
sv_tig_idx += 1
for sv_path in bundle_paths[1:]:
print >> sv_tig_paths, ">%04d-%04d %s" % ( bundle_index, sv_tig_idx, " ".join(sv_path) )
ASM_graph.add_path(sv_path, ctg="%04d" % bundle_index)
subseqs = []
for i in range(len(sv_path) - 1):
v, w = sv_path[i:i+2]
edges_to_be_removed.add( (v,w) )
uedges = u_edges[ (v,w) ]
uedges.sort( key= lambda x: len(x[0]) )
subseqs.append( uedges[-1][1] )
visited_u_edges.add( "-".join(uedges[-1][0]) )
for ue in uedges:
if "-".join(ue[0]) not in visited_u_edges:
visited_u_edges.add("-".join(ue[0]))
extra_u_edges.append(ue)
seq = "".join(subseqs)
if len(seq) > 0:
print >> sv_tigs, ">%04d-%04d %s-%s" % (bundle_index, sv_tig_idx, sv_path[0], sv_path[-1])
print >> sv_tigs, "".join(subseqs)
sv_tig_idx += 1
for u_path, seq in extra_u_edges:
#u_path = u_path.split("-")
ASM_graph.add_edge(u_path[0], u_path[-1], ctg="%04d" % bundle_index)
print >> sv_tig_paths, ">%04d-%04d-u %s" % ( bundle_index, sv_tig_idx, " ".join(u_path) )
print >> sv_tigs, ">%04d-%04d-u %s-%s" % (bundle_index, sv_tig_idx, u_path[0], u_path[-1])
print >> sv_tigs, seq
sv_tig_idx += 1
bundle_index += 1
else:
#TODO, consolidate code here
v,w = path
uedges = u_edges[ (v,w) ]
uedges.sort( key= lambda x: len(x[0]) )
subseqs.append( uedges[-1][1] )
seq = "".join(subseqs)
print >> sv_tig_paths, ">%04d-%04d %s" % ( bundle_index, sv_tig_idx, " ".join(paths) )
print >> sv_tigs, ">%04d-%04d-u %s-%s" % (bundle_index, sv_tig_idx, path[0], path[-1])
print >> sv_tigs, seq
sv_tig_idx += 1
bundle_index += 1
bundle_graph_edges = zip(path[:-1],path[1:])
#clean up the graph
edges = set(G.edges())
edges_to_be_removed |= set(bundle_graph_edges)
if DEBUG_LOG_LEVEL > 2:
print "BGE",bundle_graph_edges
edge_remove_count = 0
for v, w in edges_to_be_removed:
if (v, w) in edges:
G.remove_edge( v, w )
edge_remove_count += 1
if DEBUG_LOG_LEVEL > 2:
print "remove edge", bundle_index, w, v
edges = set(G.edges())
for v, w in edges_to_be_removed:
r_id, end = v.split(":")
end = "E" if end == "B" else "B"
v = r_id + ":" + end
r_id, end = w.split(":")
end = "E" if end == "B" else "B"
w = r_id + ":" + end
if (w, v) in edges:
G.remove_edge( w, v )
edge_remove_count += 1
if DEBUG_LOG_LEVEL > 2:
print "remove edge", bundle_index, w, v
if edge_remove_count == 0:
break
nodes = G.nodes()
for n in nodes:
if G.in_degree(n) == 0 and G.out_degree(n) == 0:
G.remove_node(n)
if DEBUG_LOG_LEVEL > 2:
print "remove node", n
sv_tig_paths.close()
sv_tigs.close()
main_tig_paths.close()
out_f.close()
bundle_edge_out.close()
return ASM_graph
def SGToNXG(sg):
G=nx.DiGraph()
max_score = max([ sg.edges[ e ].attr["score"] for e in sg.edges if sg.e_reduce[e] != True ])
out_f = open("edges_list","w")
for v, w in sg.edges:
if sg.e_reduce[(v, w)] != True:
##if 1:
out_degree = len(sg.nodes[v].out_edges)
G.add_node( v, size = out_degree )
G.add_node( w, size = out_degree )
label = sg.edges[ (v, w) ].attr["label"]
score = sg.edges[ (v, w) ].attr["score"]
print >>out_f, v, w, label, score
G.add_edge( v, w, label = label, weight = 0.001*score, n_weight = max_score - score )
#print in_node_name, out_node_name
out_f.close()
return G
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description='a example string graph assembler that is desinged for handling diploid genomes')
parser.add_argument('overlap_file', help='a file that contains the overlap information.')
parser.add_argument('read_fasta', help='the file that contains the sequence to be assembled')
parser.add_argument('--min_len', type=int, default=4000,
help='minimum length of the reads to be considered for assembling')
parser.add_argument('--min_idt', type=float, default=96,
help='minimum alignment identity of the reads to be considered for assembling')
parser.add_argument('--disable_chimer_prediction', action="store_true", default=False,
help='you may want to disable this as some reads can be falsely identified as chimers in low coverage case')
args = parser.parse_args()
overlap_file = args.overlap_file
read_fasta = args.read_fasta
seqs = {}
# load all p-reads into memory
f = FastaReader(read_fasta)
for r in f:
seqs[r.name] = r.sequence.upper()
G=nx.Graph()
edges =set()
overlap_data = []
contained_reads = set()
overlap_count = {}
# loop through the overlapping data to load the data in the a python array
# contained reads are identified
with open(overlap_file) as f:
for l in f:
l = l.strip().split()
#work around for some ill formed data recored
if len(l) != 13:
continue
f_id, g_id, score, identity = l[:4]
if f_id == g_id: # don't need self-self overlapping
continue
if g_id not in seqs:
continue
if f_id not in seqs:
continue
score = int(score)
identity = float(identity)
contained = l[12]
if contained == "contained":
contained_reads.add(f_id)
continue
if contained == "contains":
contained_reads.add(g_id)
continue
if contained == "none":
continue
if identity < args.min_idt: # only take record with >96% identity as overlapped reads
continue
#if score > -2000:
# continue
f_strain, f_start, f_end, f_len = (int(c) for c in l[4:8])
g_strain, g_start, g_end, g_len = (int(c) for c in l[8:12])
# only used reads longer than the 4kb for assembly
if f_len < args.min_len: continue
if g_len < args.min_len: continue
# double check for proper overlap
if f_start > 24 and f_len - f_end > 24: # allow 24 base tolerance on both sides of the overlapping
continue
if g_start > 24 and g_len - g_end > 24:
continue
if g_strain == 0:
if f_start < 24 and g_len - g_end > 24:
continue
if g_start < 24 and f_len - f_end > 24:
continue
else:
if f_start < 24 and g_start > 24:
continue
if g_start < 24 and f_start > 24:
continue
overlap_data.append( (f_id, g_id, score, identity,
f_strain, f_start, f_end, f_len,
g_strain, g_start, g_end, g_len) )
overlap_count[f_id] = overlap_count.get(f_id,0)+1
overlap_count[g_id] = overlap_count.get(g_id,0)+1
overlap_set = set()
sg = StringGraph()
for od in overlap_data:
f_id, g_id, score, identity = od[:4]
if f_id in contained_reads:
continue
if g_id in contained_reads:
continue
f_s, f_b, f_e, f_l = od[4:8]
g_s, g_b, g_e, g_l = od[8:12]
overlap_pair = [f_id, g_id]
overlap_pair.sort()
overlap_pair = tuple( overlap_pair )
if overlap_pair in overlap_set: # don't allow duplicated records
continue
else:
overlap_set.add(overlap_pair)
if g_s == 1: # revered alignment, swapping the begin and end coordinates
g_b, g_e = g_e, g_b
# build the string graph edges for each overlap
if f_b > 24:
if g_b < g_e:
"""
f.B f.E
f ----------->
g ------------->
g.B g.E
"""
if f_b == 0 or g_e - g_l == 0:
continue
sg.add_edge( "%s:B" % g_id, "%s:B" % f_id, label = "%s:%d-%d" % (f_id, f_b, 0),
length = abs(f_b-0),
score = -score)
sg.add_edge( "%s:E" % f_id, "%s:E" % g_id, label = "%s:%d-%d" % (g_id, g_e, g_l),
length = abs(g_e-g_l),
score = -score)
else:
"""
f.B f.E
f ----------->
g <-------------
g.E g.B
"""
if f_b == 0 or g_e == 0:
continue
sg.add_edge( "%s:E" % g_id, "%s:B" % f_id, label = "%s:%d-%d" % (f_id, f_b, 0),
length = abs(f_b -0),
score = -score)
sg.add_edge( "%s:E" % f_id, "%s:B" % g_id, label = "%s:%d-%d" % (g_id, g_e, 0),
length = abs(g_e- 0),
score = -score)
else:
if g_b < g_e:
"""
f.B f.E
f ----------->
g ------------->
g.B g.E
"""
if g_b == 0 or f_e - f_l == 0:
continue
sg.add_edge( "%s:B" % f_id, "%s:B" % g_id, label = "%s:%d-%d" % (g_id, g_b, 0),
length = abs(g_b - 0),
score = -score)
sg.add_edge( "%s:E" % g_id, "%s:E" % f_id, label = "%s:%d-%d" % (f_id, f_e, f_l),
length = abs(f_e-f_l),
score = -score)
else:
"""
f.B f.E
f ----------->
g <-------------
g.E g.B
"""
if g_b - g_l == 0 or f_e - f_l ==0:
continue
sg.add_edge( "%s:B" % f_id, "%s:E" % g_id, label = "%s:%d-%d" % (g_id, g_b, g_l),
length = abs(g_b - g_l),
score = -score)
sg.add_edge( "%s:B" % g_id, "%s:E" % f_id, label = "%s:%d-%d" % (f_id, f_e, f_l),
length = abs(f_e - f_l),
score = -score)
sg.init_reduce_dict()
if not args.disable_chimer_prediction:
sg.mark_chimer_edge()
sg.mark_spur_edge()
sg.mark_tr_edges() # mark those edges that transitive redundant
if DEBUG_LOG_LEVEL > 1:
print sum( [1 for c in sg.e_reduce.values() if c == True] )
print sum( [1 for c in sg.e_reduce.values() if c == False] )
sg.mark_best_overlap() # mark those edges that are best overlap edges
if DEBUG_LOG_LEVEL > 1:
print sum( [1 for c in sg.e_reduce.values() if c == False] )
G = SGToNXG(sg)
#nx.write_gexf(G, "string_graph.gexf") # output the raw string string graph for visuliation
nx.write_adjlist(G, "string_graph.adj") # write out the whole adjacent list of the string graph
u_edges = generate_unitig(sg, seqs, out_fn = "unitgs.fa") # reduct to string graph into unitig graph
ASM_graph = get_bundles(u_edges ) # get the assembly
#nx.write_gexf(ASM_graph, "asm_graph.gexf")
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