def _trim_change_point(self, cp):
# search for matches in change point interval
num_trimmed = 0
if cp.sign < 0:
hits = self.tree_ends.find(cp.pos, cp.end)
for hit in hits:
t = hit.value
# last exon start cannot overlap interval
last_exon_start = t.exons[-1][0]
if cp.pos <= last_exon_start <= cp.end:
continue
# trim end left
t.exons[-1] = Exon(last_exon_start, cp.pos)
num_trimmed += 1
else:
hits = self.tree_starts.find(cp.start, cp.pos)
for hit in hits:
t = hit.value
# first exon end cannot overlap interval
first_exon_end = t.exons[0][1]
if cp.start <= first_exon_end <= cp.pos:
continue
# trim start right
t.exons[0] = Exon(cp.pos, first_exon_end)
num_trimmed += 1
def get_transcripts(stream):
"""Collect transcripts as collections of CDS/exons"""
transcripts = {}
for line__ in stream:
if isinstance(line__, str):
line = line__
elif isinstance(line__, bytes):
line = line__.decode()
else:
raise InputError('Unsupported stream data')
if line.startswith('#') or not line.strip():
continue
f = parse(line)
if f.feature == 'exon':
m = re.search('transcript_id "([a-zA-Z0-9\.]+)";', f.attribute)
if not m:
raise ValueError('Gene id could not be found')
index = m.group(1)
exons = []
if index not in transcripts:
transcripts[index] = mRNA(seqid = f.seqid, start = None,
end = None, strand = f.strand,
exons = [], attributes = '')
transcripts[index].exons.append(Exon(seqid = f.seqid,
start = f.start, end = f.end,
strand = f.strand))
return transcripts
def _create_splice_graph(self):
'''returns networkx DiGraph object'''
G = nx.DiGraph()
node_bounds = self.node_bounds
node_id_map = {}
id_node_map = {}
current_id = 2
for t in self.itertransfrags():
# split exons that cross boundaries and get the
# nodes that made up the transfrag
nodes = []
for n in split_transfrag(t, node_bounds):
n = Exon(*n)
if n not in node_id_map:
n_id = current_id
current_id += 1
node_id_map[n] = n_id
id_node_map[n_id] = n
else:
n_id = node_id_map[n]
nodes.append(n_id)
if t.strand == Strand.NEG:
nodes.reverse()
# add nodes/edges to graph
u = nodes[0]
SGNode.add(G, u, id_node_map[u], is_ref=t.is_ref)
for i in xrange(1, len(nodes)):
v = nodes[i]
SGNode.add(G, v, id_node_map[v], is_ref=t.is_ref)
G.add_edge(u, v)
u = v
G.graph['node_id_map'] = node_id_map
G.graph['id_node_map'] = id_node_map
return G
def parse_gtf(gtf_lines, ignore_ref=True):
'''
returns OrderedDict key is transcript_id value is Transfrag
'''
t_dict = collections.OrderedDict()
for gtf_line in gtf_lines:
f = GTF.Feature.from_str(gtf_line)
t_id = f.attrs[GTF.Attr.TRANSCRIPT_ID]
is_ref = bool(int(f.attrs.get(GTF.Attr.REF, '0')))
if is_ref and ignore_ref:
continue
if f.feature == 'transcript':
if t_id in t_dict:
raise GTFError("Transcript '%s' duplicate detected" % t_id)
t = Transfrag.from_gtf(f)
t_dict[t_id] = t
elif f.feature == 'exon':
if t_id not in t_dict:
logging.error('Feature: "%s"' % str(f))
raise GTFError("Transcript '%s' exon feature appeared in "
"gtf file prior to transcript feature" %
t_id)
t = t_dict[t_id]
t.exons.append(Exon(f.start, f.end))
return t_dict
def _create_splice_graph(self):
G = SGraph()
for t in self.itertransfrags():
nodes = [Exon(*n) for n in split_transfrag(t, self.node_bounds)]
if self.strand == Strand.NEG:
nodes.reverse()
G.add_path(nodes, t.is_ref)
# after graph is built, set node properties for start and stop nodes
G.set_start_stop_nodes()
return G
def reconstruct_exons(self, path):
# reverse negative stranded data so that all paths go from
# small -> large genomic coords
if self.strand == Strand.NEG:
path.reverse()
# convert from integer node labels to genome (start, end) tuples
path = [self.get_node_interval(nid) for nid in path]
# collapse contiguous nodes along path
newpath = []
chain = [path[0]]
for v in path[1:]:
if chain[-1].end != v.start:
# update path with merge chain node
newpath.append(Exon(chain[0].start, chain[-1].end))
# reset chain
chain = []
chain.append(v)
# add last chain
newpath.append(Exon(chain[0].start, chain[-1].end))
return newpath
def reconstruct_path(kmer_path, id_kmer_map, sgraph):
# reconstruct path from kmer ids
path = list(id_kmer_map[kmer_path[1]])
path.extend(id_kmer_map[n][-1] for n in kmer_path[2:-1])
# reverse negative stranded data so that all paths go from
# small -> large genomic coords
if sgraph.strand == Strand.NEG:
path.reverse()
# convert from integer node labels to genome (start, end) tuples
path = [sgraph.get_node_interval(nid) for nid in path]
# collapse contiguous nodes along path
newpath = []
chain = [path[0]]
for v in path[1:]:
if chain[-1].end != v.start:
# update path with merge chain node
newpath.append(Exon(chain[0].start, chain[-1].end))
# reset chain
chain = []
chain.append(v)
# add last chain
newpath.append(Exon(chain[0].start, chain[-1].end))
return newpath
def parse_gtf(gtf_iter, sample_id, gtf_expr_attr, is_ref):
'''
returns list of Transfrag objects
'''
t_dict = collections.OrderedDict()
total_expr = 0.0
cur_t_id = 1
for gtf_line in gtf_iter:
if not gtf_line:
continue
if not gtf_line.strip():
continue
if gtf_line.startswith("#"):
continue
f = GTF.Feature.from_str(gtf_line)
if f.feature == 'transcript':
t_id = f.attrs[GTF.Attr.TRANSCRIPT_ID]
if t_id in t_dict:
raise GTFError("Transcript '%s' duplicate detected" % t_id)
# rename transcript id
new_t_id = "%s.%d" % (sample_id, cur_t_id)
cur_t_id += 1
# parse expression
if is_ref:
expr = 0.0
else:
if gtf_expr_attr not in f.attrs:
raise GTFError("GTF expression attribute '%s' not found" %
(gtf_expr_attr))
expr = float(f.attrs[gtf_expr_attr])
total_expr += expr
# create transfrag
t = Transfrag(chrom=f.seqid,
strand=Strand.from_gtf(f.strand),
_id=new_t_id,
expr=float(expr),
is_ref=is_ref,
exons=None)
t_dict[t_id] = t
elif f.feature == 'exon':
t_id = f.attrs[GTF.Attr.TRANSCRIPT_ID]
if t_id not in t_dict:
logging.error('Feature: "%s"' % str(f))
raise GTFError("Transcript '%s' exon feature appeared in "
"gtf file prior to transcript feature" % t_id)
t = t_dict[t_id]
t.exons.append(Exon(f.start, f.end))
return t_dict.values(), total_expr
def get_chains(G, introns=True):
"""
group nodes into chains
returns a dict mapping node -> chain, as well as a
dict mapping chains to nodes
"""
if introns:
can_collapse_func = can_collapse
else:
can_collapse_func = can_collapse_contiguous
imin2 = lambda x, y: x if x <= y else y
imax2 = lambda x, y: x if x >= y else y
node_chain_map = {}
chains = {}
# initialize each node to be in a "chain" by itself
for n in G.nodes_iter():
node_chain_map[n] = n
chains[n] = set((n, ))
for u, v in G.edges_iter():
if not can_collapse_func(G, u, v):
continue
# get chains containing these nodes
u_new = node_chain_map[u]
u_chain = chains[u_new]
del chains[u_new]
v_new = node_chain_map[v]
v_chain = chains[v_new]
del chains[v_new]
# merge chains
merged_chain = u_chain.union(v_chain)
merged_node = Exon(imin2(u_new.start, v_new.start),
imax2(u_new.end, v_new.end))
# point all nodes in chain to new parent
for n in merged_chain:
node_chain_map[n] = merged_node
chains[merged_node] = merged_chain
# sort chain nodes by genome position and store as list
for parent in chains:
chains[parent] = sorted(chains[parent],
key=operator.attrgetter('start'))
return node_chain_map, chains
def from_bed(line):
fields = line.strip().split('\t')
chrom = fields[0]
tx_start = int(fields[1])
_id = fields[3]
is_ref = (_id.split('.')[0] == Sample.REF_ID)
expr = float(fields[4])
strand = Strand.from_bed(fields[5])
num_exons = int(fields[9])
block_sizes = fields[10].split(',')
block_starts = fields[11].split(',')
exons = []
for i in xrange(num_exons):
start = tx_start + int(block_starts[i])
end = start + int(block_sizes[i])
exons.append(Exon(start, end))
return Transfrag(chrom=chrom,
strand=strand,
_id=_id,
expr=expr,
is_ref=is_ref,
exons=exons)
def parse_gtf(gtf_lines):
'''
returns list of Transfrag objects
'''
t_dict = collections.OrderedDict()
for gtf_line in gtf_lines:
f = GTF.Feature.from_str(gtf_line)
if f.feature == 'transcript':
t_id = f.attrs[GTF.Attr.TRANSCRIPT_ID]
if t_id in t_dict:
raise GTFError("Transcript '%s' duplicate detected" % t_id)
t = Transfrag.from_gtf(f)
t_dict[t_id] = t
elif f.feature == 'exon':
t_id = f.attrs[GTF.Attr.TRANSCRIPT_ID]
if t_id not in t_dict:
logging.error('Feature: "%s"' % str(f))
raise GTFError("Transcript '%s' exon feature appeared in "
"gtf file prior to transcript feature" %
t_id)
t = t_dict[t_id]
t.exons.append(Exon(f.start, f.end))
return t_dict
def get_mrnas(stream, source=None):
"""Collect mRNA extents with exons"""
mrnas = {}
for line__ in stream:
if isinstance(line__, str):
line = line__
elif isinstance(line__, bytes):
line = line__.decode()
else:
raise InputError('Unsupported stream data')
if line.startswith('#'):
continue
f = parse(line)
if source is not None and f.source != source:
continue
# if f.feature in ['mRNA', 'transcript', 'primary_transcript', 'miRNA',
# 'lnc_RNA', 'gene', 'snoRNA', 'antisense_RNA']:
# if f.feature in ['mRNA', 'transcript']:
if f.feature == 'mRNA':
# m = re.search('ID=(rna\d\d*);', f.attribute)
m = re.search('ID=(\w[\w:]*)', f.attribute)
if not m:
raise ValueError('mRNA id could not be found')
index = m.group(1)
exons = []
if index in mrnas:
if mrnas[index].start is not None:
raise RuntimeError(
'mRNA with the same id is already present')
exons = mrna[index].exons
attributes = {}
for a in f.attribute.rstrip().split(';'):
r = a.split('=')
if (len(r) == 2):
attributes[r[0]] = r[1]
mrnas[index] = mRNA(seqid=f.seqid,
start=f.start,
end=f.end,
strand=f.strand,
exons=exons,
attributes=attributes)
if f.feature == 'exon':
# m = re.search('Parent=(rna\d\d*)', f.attribute)
m = re.search('Parent=(\w[\w:]*)', f.attribute)
if not m:
# there seem to be exons not assigned to mRNAs
# raise ValueError('Exon without parent: {0}'.format(f.attribute))
print('WARNING: Exon without parent: {0}'.format(line))
continue
index = m.group(1)
if index not in mrnas:
# raise ValueError('Parent of the exon not found: {0}'.format(line))
# mrnas[index] = mRNA(seqid = f.seqid, start = None, end = None,
# strand = None, exons = [])
continue
mrnas[index].exons.append(
Exon(seqid=f.seqid, start=f.start, end=f.end, strand=f.strand))
# these things appear in RNA-seq, but are not mRNA
if f.feature == 'five_prime_UTR':
m = re.search('ID=(id\d\d*);', f.attribute)
if not m:
raise ValueError("5'UTR id could not be found")
index = m.group(1)
if index not in mrnas:
mrnas[index] = mRNA(seqid=f.seqid,
start=None,
end=None,
strand=None,
exons=[],
attributes={})
mrnas[index].exons.append(
Exon(seqid=f.seqid, start=f.start, end=f.end, strand=f.strand))
# this is a hack
# there are introns with no exons in the gff file, we create fake
# exons for easier processing
if f.feature == 'intron':
m = re.search('ID=(id\d\d*);', f.attribute)
if not m:
raise ValueError('Intron id could not be found')
index = m.group(1)
if index in mrnas:
raise RuntimeError('mRNA element already present')
exon1 = Exon(seqid=f.seqid,
start=f.start - 2,
end=f.start - 1,
strand=f.strand)
exon2 = Exon(seqid=f.seqid,
start=f.end + 1,
end=f.end + 2,
strand=f.strand)
mrnas[index] = mRNA(seqid=f.seqid,
start=None,
end=None,
strand=None,
exons=[exon1, exon2],
attributes={})
return mrnas
