def categorize(transcripts):
# find the intron domains of the transcripts
boundaries = find_exon_boundaries(transcripts)
cds_sense = set()
cds_unstranded = set()
test_transcripts = []
for t in transcripts:
if 'cds' in t.attrs:
for n in split_exons(t, boundaries):
cds_sense.add((t.strand, n[0], n[1]))
cds_unstranded.add(n)
else:
test_transcripts.append(t)
node_dict = {}
noncoding_transcripts = []
utr_unstranded = set()
for t in test_transcripts:
mcat = t.attrs['category']
tcat = t.attrs['transcript_category']
has_cds = False
nodes = []
for n in split_exons(t, boundaries):
nstrand = (t.strand, n[0], n[1])
nodes.append(nstrand)
if ((t.strand == NO_STRAND) and (n in cds_unstranded)):
node_dict[nstrand] = (tcat, mcat, 'cds')
has_cds = True
elif nstrand in cds_sense:
node_dict[nstrand] = (tcat, mcat, 'cds')
has_cds = True
if has_cds:
for n in nodes:
if n not in node_dict:
node_dict[nstrand] = (tcat, mcat, 'utr')
utr_unstranded.add((n[1], n[2]))
else:
noncoding_transcripts.append(t)
for t in noncoding_transcripts:
mcat = t.attrs['category']
tcat = t.attrs['transcript_category']
for n in split_exons(t, boundaries):
nstrand = (t.strand, n[0], n[1])
if nstrand not in node_dict:
# check antisense
if n in cds_unstranded:
node_dict[nstrand] = (tcat, mcat, 'cds_antisense')
elif n in utr_unstranded:
node_dict[nstrand] = (tcat, mcat, 'utr_antisense')
else:
node_dict[nstrand] = (tcat, mcat, 'noncoding')
for n, d in node_dict.iteritems():
strand, start, end = n
tcat, mcat, ccat = d
yield start, end, strand, tcat, mcat, ccat
def categorize(transcripts):
# find the intron domains of the transcripts
boundaries = find_exon_boundaries(transcripts)
cds_sense = set()
cds_unstranded = set()
test_transcripts = []
for t in transcripts:
if 'cds' in t.attrs:
for n in split_exons(t, boundaries):
cds_sense.add((t.strand, n[0], n[1]))
cds_unstranded.add(n)
else:
test_transcripts.append(t)
node_dict = {}
noncoding_transcripts = []
utr_unstranded = set()
for t in test_transcripts:
mcat = t.attrs['category']
tcat = t.attrs['transcript_category']
has_cds = False
nodes = []
for n in split_exons(t, boundaries):
nstrand = (t.strand, n[0], n[1])
nodes.append(nstrand)
if ((t.strand == NO_STRAND) and (n in cds_unstranded)):
node_dict[nstrand] = (tcat, mcat, 'cds')
has_cds = True
elif nstrand in cds_sense:
node_dict[nstrand] = (tcat, mcat, 'cds')
has_cds = True
if has_cds:
for n in nodes:
if n not in node_dict:
node_dict[nstrand] = (tcat, mcat, 'utr')
utr_unstranded.add((n[1],n[2]))
else:
noncoding_transcripts.append(t)
for t in noncoding_transcripts:
mcat = t.attrs['category']
tcat = t.attrs['transcript_category']
for n in split_exons(t, boundaries):
nstrand = (t.strand, n[0], n[1])
if nstrand not in node_dict:
# check antisense
if n in cds_unstranded:
node_dict[nstrand] = (tcat, mcat, 'cds_antisense')
elif n in utr_unstranded:
node_dict[nstrand] = (tcat, mcat, 'utr_antisense')
else:
node_dict[nstrand] = (tcat, mcat, 'noncoding')
for n,d in node_dict.iteritems():
strand, start, end = n
tcat, mcat, ccat = d
yield start, end, strand, tcat, mcat, ccat
def create_undirected_transcript_graph(transcripts, add_node_func, **kwargs):
'''
add all transcripts to a single undirected graph
'''
# find the intron domains of the transcripts
boundaries = find_exon_boundaries(transcripts)
# initialize transcript graph as undirected at first
G = nx.Graph()
# add transcripts
for t in transcripts:
# split exons that cross boundaries and to get the
# nodes in the transcript path
nodes = list(Exon(start,end) for start,end in split_exons(t, boundaries))
# add nodes/edges to graph
u = nodes[0]
add_node_func(G, u, t, **kwargs)
for v in nodes[1:]:
add_node_func(G, v, t, **kwargs)
G.add_edge(u, v)
u = v
return G
def annotate_locus(transcripts,
gtf_sample_attr):
# store reference introns
# (strand,start,end) -> ids (set)
ref_intron_dict = collections.defaultdict(lambda: [])
ref_node_dict = collections.defaultdict(lambda: ([],[]))
node_score_dict = collections.defaultdict(lambda: [0.0, 0.0])
all_introns = set()
# find the intron domains of the transcripts
boundaries = find_exon_boundaries(transcripts)
# add transcript to intron and graph data structures
inp_transcripts = []
for t in transcripts:
# separate ref and nonref transcripts
is_ref = bool(int(t.attrs[GTFAttr.REF]))
if is_ref:
# split exons that cross boundaries and get the
# nodes in the transcript path
for n in split_exons(t, boundaries):
ref_node_dict[n][t.strand].append(t)
# add to introns
for start,end in t.iterintrons():
ref_intron_dict[(t.strand, start, end)].append(t)
all_introns.add((t.strand,start,end))
else:
if t.strand != NO_STRAND:
score = float(t.attrs[GTFAttr.SCORE])
for n in split_exons(t, boundaries):
node_score_dict[n][t.strand] += score
inp_transcripts.append(t)
# add to introns
for start,end in t.iterintrons():
all_introns.add((t.strand,start,end))
# index introns for fast intersection
intron_tree = IntervalTree()
for strand,start,end in all_introns:
intron_tree.insert_interval(Interval(start,end,strand=strand))
del all_introns
# categorize transcripts
strand_transcript_lists = [[], [], []]
for t in inp_transcripts:
# get transcript nodes and introns
nodes = list(split_exons(t, boundaries))
introns = set(t.iterintrons())
# try to resolve strand
strand = t.strand
if strand == NO_STRAND:
strand = resolve_strand(nodes, node_score_dict, ref_node_dict)
# define opposite strand
if strand == NO_STRAND:
opp_strand = NO_STRAND
else:
opp_strand = (strand + 1) % 2
# get all reference transcripts that share introns
intron_ref_dict = {}
for start,end in introns:
if (strand, start, end) in ref_intron_dict:
refs = ref_intron_dict[(strand, start, end)]
intron_ref_dict.update((ref.attrs[GTFAttr.TRANSCRIPT_ID],ref)
for ref in refs)
intron_refs = []
for ref in intron_ref_dict.itervalues():
intron_refs.append((ref,list(split_exons(ref, boundaries))))
# get all reference transcripts that share coverage
same_strand_ref_dict = {}
opp_strand_ref_dict = {}
for n in nodes:
if n in ref_node_dict:
strand_refs = ref_node_dict[n]
same_strand_ref_dict.update((ref.attrs[GTFAttr.TRANSCRIPT_ID],ref)
for ref in strand_refs[strand])
opp_strand_ref_dict.update((ref.attrs[GTFAttr.TRANSCRIPT_ID],ref)
for ref in strand_refs[opp_strand])
same_strand_refs = []
for ref in same_strand_ref_dict.itervalues():
same_strand_refs.append((ref,list(split_exons(ref, boundaries))))
opp_strand_refs = []
for ref in opp_strand_ref_dict.itervalues():
opp_strand_refs.append((ref,list(split_exons(ref, boundaries))))
# categorize
cinf = categorize_transcript(t, nodes, introns,
intron_refs,
same_strand_refs,
opp_strand_refs,
intron_tree,
ignore_test=False)
if cinf.is_test:
# recategorize test transcripts
cinf2 = categorize_transcript(t, nodes, introns,
intron_refs,
same_strand_refs,
opp_strand_refs,
intron_tree,
ignore_test=True)
cinf = cinf._replace(category=cinf2.category)
# add annotation attributes
best_ref_id = (cinf.ref.attrs[GTFAttr.TRANSCRIPT_ID]
if cinf.ref is not None else 'na')
t.attrs[GTFAttr.CATEGORY] = cinf.category
t.attrs[GTFAttr.TEST] = '1' if cinf.is_test else '0'
t.attrs[GTFAttr.ANN_REF_ID] = best_ref_id
t.attrs[GTFAttr.ANN_COV_RATIO] = cinf.ann_cov_ratio
t.attrs[GTFAttr.ANN_INTRON_RATIO] = cinf.ann_intron_ratio
# group transcripts by strand
strand_transcript_lists[strand].append(t)
# explictly delete large data structures
del ref_intron_dict
del ref_node_dict
del node_score_dict
del intron_tree
del inp_transcripts
# annotate score and recurrence for transcripts
for strand_transcripts in strand_transcript_lists:
# find the intron domains of the transcripts
boundaries = find_exon_boundaries(strand_transcripts)
# gather node score/recurrence data
new_data_func = lambda: {'ids': set(),
'score': 0.0,
'pct': 0.0}
node_data = collections.defaultdict(new_data_func)
for t in strand_transcripts:
sample_id = t.attrs[gtf_sample_attr]
score = float(t.attrs[GTFAttr.SCORE])
pctrank = float(t.attrs[GTFAttr.PCTRANK])
# split exons that cross boundaries and to get the
# nodes in the transcript path
for n in split_exons(t, boundaries):
nd = node_data[n]
nd['ids'].add(sample_id)
nd['score'] += score
nd['pct'] += pctrank
# calculate recurrence and score statistics
for t in strand_transcripts:
nodes = list(split_exons(t, boundaries))
mean_score, mean_pctrank, mean_recur = \
compute_recurrence_and_score(nodes, node_data)
t.attrs[GTFAttr.MEAN_SCORE] = mean_score
t.attrs[GTFAttr.MEAN_PCTRANK] = mean_pctrank
t.attrs[GTFAttr.MEAN_RECURRENCE] = mean_recur
def annotate_locus(transcripts, gtf_sample_attr):
# store reference introns
# (strand,start,end) -> ids (set)
ref_intron_dict = collections.defaultdict(lambda: [])
ref_node_dict = collections.defaultdict(lambda: ([], []))
node_score_dict = collections.defaultdict(lambda: [0.0, 0.0])
all_introns = set()
# find the intron domains of the transcripts
boundaries = find_exon_boundaries(transcripts)
# add transcript to intron and graph data structures
inp_transcripts = []
for t in transcripts:
# separate ref and nonref transcripts
is_ref = bool(int(t.attrs[GTFAttr.REF]))
if is_ref:
# split exons that cross boundaries and get the
# nodes in the transcript path
for n in split_exons(t, boundaries):
ref_node_dict[n][t.strand].append(t)
# add to introns
for start, end in t.iterintrons():
ref_intron_dict[(t.strand, start, end)].append(t)
all_introns.add((t.strand, start, end))
else:
if t.strand != NO_STRAND:
score = float(t.attrs[GTFAttr.SCORE])
for n in split_exons(t, boundaries):
node_score_dict[n][t.strand] += score
inp_transcripts.append(t)
# add to introns
for start, end in t.iterintrons():
all_introns.add((t.strand, start, end))
# index introns for fast intersection
intron_tree = IntervalTree()
for strand, start, end in all_introns:
intron_tree.insert_interval(Interval(start, end, strand=strand))
del all_introns
# categorize transcripts
strand_transcript_lists = [[], [], []]
for t in inp_transcripts:
# get transcript nodes and introns
nodes = list(split_exons(t, boundaries))
introns = set(t.iterintrons())
# try to resolve strand
strand = t.strand
if strand == NO_STRAND:
strand = resolve_strand(nodes, node_score_dict, ref_node_dict)
# define opposite strand
if strand == NO_STRAND:
opp_strand = NO_STRAND
else:
opp_strand = (strand + 1) % 2
# get all reference transcripts that share introns
intron_ref_dict = {}
for start, end in introns:
if (strand, start, end) in ref_intron_dict:
refs = ref_intron_dict[(strand, start, end)]
intron_ref_dict.update(
(ref.attrs[GTFAttr.TRANSCRIPT_ID], ref) for ref in refs)
intron_refs = []
for ref in intron_ref_dict.itervalues():
intron_refs.append((ref, list(split_exons(ref, boundaries))))
# get all reference transcripts that share coverage
same_strand_ref_dict = {}
opp_strand_ref_dict = {}
for n in nodes:
if n in ref_node_dict:
strand_refs = ref_node_dict[n]
same_strand_ref_dict.update(
(ref.attrs[GTFAttr.TRANSCRIPT_ID], ref)
for ref in strand_refs[strand])
opp_strand_ref_dict.update(
(ref.attrs[GTFAttr.TRANSCRIPT_ID], ref)
for ref in strand_refs[opp_strand])
same_strand_refs = []
for ref in same_strand_ref_dict.itervalues():
same_strand_refs.append((ref, list(split_exons(ref, boundaries))))
opp_strand_refs = []
for ref in opp_strand_ref_dict.itervalues():
opp_strand_refs.append((ref, list(split_exons(ref, boundaries))))
# categorize
cinf = categorize_transcript(t,
nodes,
introns,
intron_refs,
same_strand_refs,
opp_strand_refs,
intron_tree,
ignore_test=False)
if cinf.is_test:
# recategorize test transcripts
cinf2 = categorize_transcript(t,
nodes,
introns,
intron_refs,
same_strand_refs,
opp_strand_refs,
intron_tree,
ignore_test=True)
cinf = cinf._replace(category=cinf2.category)
# add annotation attributes
best_ref_id = (cinf.ref.attrs[GTFAttr.TRANSCRIPT_ID]
if cinf.ref is not None else 'na')
t.attrs[GTFAttr.CATEGORY] = cinf.category
t.attrs[GTFAttr.TEST] = '1' if cinf.is_test else '0'
t.attrs[GTFAttr.ANN_REF_ID] = best_ref_id
t.attrs[GTFAttr.ANN_COV_RATIO] = cinf.ann_cov_ratio
t.attrs[GTFAttr.ANN_INTRON_RATIO] = cinf.ann_intron_ratio
# group transcripts by strand
strand_transcript_lists[strand].append(t)
# explictly delete large data structures
del ref_intron_dict
del ref_node_dict
del node_score_dict
del intron_tree
del inp_transcripts
# annotate score and recurrence for transcripts
for strand_transcripts in strand_transcript_lists:
# find the intron domains of the transcripts
boundaries = find_exon_boundaries(strand_transcripts)
# gather node score/recurrence data
new_data_func = lambda: {'ids': set(), 'score': 0.0, 'pct': 0.0}
node_data = collections.defaultdict(new_data_func)
for t in strand_transcripts:
sample_id = t.attrs[gtf_sample_attr]
score = float(t.attrs[GTFAttr.SCORE])
pctrank = float(t.attrs[GTFAttr.PCTRANK])
# split exons that cross boundaries and to get the
# nodes in the transcript path
for n in split_exons(t, boundaries):
nd = node_data[n]
nd['ids'].add(sample_id)
nd['score'] += score
nd['pct'] += pctrank
# calculate recurrence and score statistics
for t in strand_transcripts:
nodes = list(split_exons(t, boundaries))
mean_score, mean_pctrank, mean_recur = \
compute_recurrence_and_score(nodes, node_data)
t.attrs[GTFAttr.MEAN_SCORE] = mean_score
t.attrs[GTFAttr.MEAN_PCTRANK] = mean_pctrank
t.attrs[GTFAttr.MEAN_RECURRENCE] = mean_recur
def compare_locus(transcripts):
# store reference introns
# (strand,start,end) -> ids (set)
ref_intron_dict = collections.defaultdict(lambda: [])
ref_node_dict = collections.defaultdict(lambda: [])
ref_splicing_patterns = collections.defaultdict(lambda: [])
ref_dict = {}
# find the intron domains of the transcripts
boundaries = find_exon_boundaries(transcripts)
test_transcripts = []
for t in transcripts:
# separate ref and nonref transcripts
is_ref = bool(int(t.attrs[GTFAttr.REF]))
if is_ref:
# add to dict
ref_id = t.attrs[GTFAttr.TRANSCRIPT_ID]
ref_dict[ref_id] = t
# split exons that cross boundaries and get the
# nodes in the transcript path
for n in split_exons(t, boundaries):
ref_node_dict[n].append(t)
# add to introns
splicing_pattern = []
for start, end in t.iterintrons():
intron = (t.strand, start, end)
ref_intron_dict[intron].append(t)
splicing_pattern.append(intron)
# add to splicing patterns
if len(splicing_pattern) > 0:
ref_splicing_patterns[tuple(splicing_pattern)].append(t)
else:
test_transcripts.append(t)
# index introns for fast intersection
intron_tree = IntervalTree()
for intron, refs in ref_intron_dict.iteritems():
strand, start, end = intron
intron_tree.insert_interval(
Interval(start, end, strand=strand, value=refs))
# categorize transcripts
for t in test_transcripts:
# get transcript nodes and introns
nodes = list(split_exons(t, boundaries))
introns = []
for start, end in t.iterintrons():
introns.append((t.strand, start, end))
splicing_pattern = tuple(introns)
# keep list of all matching ref transcripts
matches = collections.defaultdict(lambda: Match())
# dict of reference transcripts -> category -> list of nodes
for n in nodes:
if n in ref_node_dict:
# look for reference transcripts that share this node
for ref in ref_node_dict[n]:
if cmp_strand(t.strand, ref.strand):
c = Category.SAME_STRAND
else:
c = Category.OPP_STRAND
ref_id = ref.attrs[GTFAttr.TRANSCRIPT_ID]
m = matches[ref_id]
m.nodes[c].append(n)
# look for reference introns that overlap this node
for hit in intron_tree.find(*n):
if cmp_strand(t.strand, hit.strand):
c = Category.INTRONIC_SAME_STRAND
else:
c = Category.INTRONIC_OPP_STRAND
for ref in hit.value:
ref_id = ref.attrs[GTFAttr.TRANSCRIPT_ID]
m = matches[ref_id]
m.nodes[c].append(n)
# dict of introns -> list of reference transcripts
for intron in introns:
if intron in ref_intron_dict:
for ref in ref_intron_dict[intron]:
ref_id = ref.attrs[GTFAttr.TRANSCRIPT_ID]
m = matches[ref_id]
m.introns.append(intron)
# check splicing pattern matches
if len(splicing_pattern) > 0:
if splicing_pattern in ref_splicing_patterns:
for ref in ref_splicing_patterns[splicing_pattern]:
ref_id = ref.attrs[GTFAttr.TRANSCRIPT_ID]
m = matches[ref_id]
m.splicing = True
# go through the matches for this transcript and determine
# the transcript category
match_stats = []
for ref_id, m in matches.iteritems():
ref = ref_dict[ref_id]
# calculate coverage
same_strand_bp = sum(
(n[1] - n[0]) for n in m.nodes[Category.SAME_STRAND])
opp_strand_bp = sum(
(n[1] - n[0]) for n in m.nodes[Category.OPP_STRAND])
# count shared introns
num_shared_introns = len(m.introns)
# decide category for this test/ref transcript pair
if m.splicing or (num_shared_introns > 0) or (same_strand_bp > 0):
c = Category.SAME_STRAND
elif (opp_strand_bp > 0):
c = Category.OPP_STRAND
else:
# count nodes of different types
num_same_strand = len(m.nodes[Category.SAME_STRAND])
num_opp_strand = len(m.nodes[Category.OPP_STRAND])
num_intronic_same_strand = len(
m.nodes[Category.INTRONIC_SAME_STRAND])
num_intronic_opp_strand = len(
m.nodes[Category.INTRONIC_OPP_STRAND])
assert num_same_strand == 0
assert num_opp_strand == 0
num_intronic = (num_intronic_same_strand +
num_intronic_opp_strand)
assert num_intronic > 0
if (num_intronic == len(nodes)):
# completely intronic
if num_intronic_same_strand > 0:
c = Category.INTRONIC_SAME_STRAND
else:
c = Category.INTRONIC_OPP_STRAND
else:
# interleaving means some nodes intronic and other intergenic
if num_intronic_same_strand > 0:
c = Category.INTERLEAVING_SAME_STRAND
else:
c = Category.INTERLEAVING_OPP_STRAND
# create a match object
ms = MatchStats.from_transcript(t, ref)
ms.shared_same_strand_bp = same_strand_bp
ms.shared_opp_strand_bp = opp_strand_bp
ms.shared_introns = num_shared_introns
ms.shared_splicing = m.splicing
ms.category = Category.to_str(c)
ms.distance = 0
match_stats.append(ms)
yield (t, match_stats)
def compute(self, transcripts):
intron_dict = collections.defaultdict(lambda: CompareData())
node_dict = collections.defaultdict(lambda: CompareData())
splicing_pattern_dict = collections.defaultdict(lambda: CompareData())
# find the intron domains of the transcripts
boundaries = find_exon_boundaries(transcripts)
unstranded_transcripts = []
for t in transcripts:
if t.strand == NO_STRAND:
unstranded_transcripts.append(t)
continue
# separate ref and nonref transcripts
is_ref = bool(int(t.attrs[GTFAttr.REF]))
# split exons that cross boundaries and get the
# nodes in the transcript path
for n in split_exons(t, boundaries):
n = (t.strand, n[0], n[1])
if is_ref:
node_dict[n].has_ref = True
else:
d = node_dict[n]
d.has_test = True
d.category = t.attrs['category']
splicing_pattern = []
for start, end in t.iterintrons():
n = (t.strand, start, end)
if is_ref:
intron_dict[n].has_ref = True
else:
d = intron_dict[n]
d.has_test = True
d.category = t.attrs['category']
splicing_pattern.append(n)
splicing_pattern = tuple(splicing_pattern)
if len(splicing_pattern) > 0:
if is_ref:
splicing_pattern_dict[splicing_pattern].has_ref = True
else:
d = splicing_pattern_dict[splicing_pattern]
d.has_test = True
d.category = t.attrs['category']
# handle unstranded transcripts
for t in unstranded_transcripts:
# separate ref and nonref transcripts
is_ref = bool(int(t.attrs[GTFAttr.REF]))
for n in split_exons(t, boundaries):
found_node = False
for strand in (POS_STRAND, NEG_STRAND):
sn = (strand, n[0], n[1])
if sn in node_dict:
if is_ref:
node_dict[sn].has_ref = True
else:
d = node_dict[sn]
d.has_test = True
d.category = t.attrs['category']
found_node = True
if not found_node:
sn = (NO_STRAND, n[0], n[1])
if is_ref:
node_dict[sn].has_ref = True
else:
d = node_dict[sn]
d.has_test = True
d.category = t.attrs['category']
introns = list(t.iterintrons())
assert len(introns) == 0
# compile statistics
for d in intron_dict.itervalues():
if d.has_ref and d.has_test:
self.introns_both += 1
self.introns_by_category[d.category] += 1
elif d.has_ref:
self.introns_ref_only += 1
elif d.has_test:
self.introns_test_only += 1
self.introns_by_category[d.category] += 1
for d in splicing_pattern_dict.itervalues():
if d.has_ref and d.has_test:
self.patterns_both += 1
self.patterns_by_category[d.category] += 1
elif d.has_ref:
self.patterns_ref_only += 1
elif d.has_test:
self.patterns_test_only += 1
self.patterns_by_category[d.category] += 1
for n, d in node_dict.iteritems():
strand, start, end = n
length = end - start
if d.has_ref and d.has_test:
self.cov_both += length
self.cov_by_category[d.category] += length
elif d.has_ref:
self.cov_ref_only += length
elif d.has_test:
self.cov_test_only += length
self.cov_by_category[d.category] += length
def compare_locus(transcripts):
# store reference introns
# (strand,start,end) -> ids (set)
ref_intron_dict = collections.defaultdict(lambda: [])
ref_node_dict = collections.defaultdict(lambda: [])
ref_splicing_patterns = collections.defaultdict(lambda: [])
ref_dict = {}
# find the intron domains of the transcripts
boundaries = find_exon_boundaries(transcripts)
test_transcripts = []
for t in transcripts:
# separate ref and nonref transcripts
is_ref = bool(int(t.attrs[GTFAttr.REF]))
if is_ref:
# add to dict
ref_id = t.attrs[GTFAttr.TRANSCRIPT_ID]
ref_dict[ref_id] = t
# split exons that cross boundaries and get the
# nodes in the transcript path
for n in split_exons(t, boundaries):
ref_node_dict[n].append(t)
# add to introns
splicing_pattern = []
for start,end in t.iterintrons():
intron = (t.strand, start, end)
ref_intron_dict[intron].append(t)
splicing_pattern.append(intron)
# add to splicing patterns
if len(splicing_pattern) > 0:
ref_splicing_patterns[tuple(splicing_pattern)].append(t)
else:
test_transcripts.append(t)
# index introns for fast intersection
intron_tree = IntervalTree()
for intron, refs in ref_intron_dict.iteritems():
strand, start, end = intron
intron_tree.insert_interval(Interval(start,end,strand=strand,value=refs))
# categorize transcripts
for t in test_transcripts:
# get transcript nodes and introns
nodes = list(split_exons(t, boundaries))
introns = []
for start,end in t.iterintrons():
introns.append((t.strand,start,end))
splicing_pattern = tuple(introns)
# keep list of all matching ref transcripts
matches = collections.defaultdict(lambda: Match())
# dict of reference transcripts -> category -> list of nodes
for n in nodes:
if n in ref_node_dict:
# look for reference transcripts that share this node
for ref in ref_node_dict[n]:
if cmp_strand(t.strand, ref.strand):
c = Category.SAME_STRAND
else:
c = Category.OPP_STRAND
ref_id = ref.attrs[GTFAttr.TRANSCRIPT_ID]
m = matches[ref_id]
m.nodes[c].append(n)
# look for reference introns that overlap this node
for hit in intron_tree.find(*n):
if cmp_strand(t.strand, hit.strand):
c = Category.INTRONIC_SAME_STRAND
else:
c = Category.INTRONIC_OPP_STRAND
for ref in hit.value:
ref_id = ref.attrs[GTFAttr.TRANSCRIPT_ID]
m = matches[ref_id]
m.nodes[c].append(n)
# dict of introns -> list of reference transcripts
for intron in introns:
if intron in ref_intron_dict:
for ref in ref_intron_dict[intron]:
ref_id = ref.attrs[GTFAttr.TRANSCRIPT_ID]
m = matches[ref_id]
m.introns.append(intron)
# check splicing pattern matches
if len(splicing_pattern) > 0:
if splicing_pattern in ref_splicing_patterns:
for ref in ref_splicing_patterns[splicing_pattern]:
ref_id = ref.attrs[GTFAttr.TRANSCRIPT_ID]
m = matches[ref_id]
m.splicing = True
# go through the matches for this transcript and determine
# the transcript category
match_stats = []
for ref_id, m in matches.iteritems():
ref = ref_dict[ref_id]
# calculate coverage
same_strand_bp = sum((n[1] - n[0]) for n in m.nodes[Category.SAME_STRAND])
opp_strand_bp = sum((n[1] - n[0]) for n in m.nodes[Category.OPP_STRAND])
# count shared introns
num_shared_introns = len(m.introns)
# decide category for this test/ref transcript pair
if m.splicing or (num_shared_introns > 0) or (same_strand_bp > 0):
c = Category.SAME_STRAND
elif (opp_strand_bp > 0):
c = Category.OPP_STRAND
else:
# count nodes of different types
num_same_strand = len(m.nodes[Category.SAME_STRAND])
num_opp_strand = len(m.nodes[Category.OPP_STRAND])
num_intronic_same_strand = len(m.nodes[Category.INTRONIC_SAME_STRAND])
num_intronic_opp_strand = len(m.nodes[Category.INTRONIC_OPP_STRAND])
assert num_same_strand == 0
assert num_opp_strand == 0
num_intronic = (num_intronic_same_strand +
num_intronic_opp_strand)
assert num_intronic > 0
if (num_intronic == len(nodes)):
# completely intronic
if num_intronic_same_strand > 0:
c = Category.INTRONIC_SAME_STRAND
else:
c = Category.INTRONIC_OPP_STRAND
else:
# interleaving means some nodes intronic and other intergenic
if num_intronic_same_strand > 0:
c = Category.INTERLEAVING_SAME_STRAND
else:
c = Category.INTERLEAVING_OPP_STRAND
# create a match object
ms = MatchStats.from_transcript(t, ref)
ms.shared_same_strand_bp = same_strand_bp
ms.shared_opp_strand_bp = opp_strand_bp
ms.shared_introns = num_shared_introns
ms.shared_splicing = m.splicing
ms.category = Category.to_str(c)
ms.distance = 0
match_stats.append(ms)
yield (t, match_stats)
def compute(self, transcripts):
intron_dict = collections.defaultdict(lambda: CompareData())
node_dict = collections.defaultdict(lambda: CompareData())
splicing_pattern_dict = collections.defaultdict(lambda: CompareData())
# find the intron domains of the transcripts
boundaries = find_exon_boundaries(transcripts)
unstranded_transcripts = []
for t in transcripts:
if t.strand == NO_STRAND:
unstranded_transcripts.append(t)
continue
# separate ref and nonref transcripts
is_ref = bool(int(t.attrs[GTFAttr.REF]))
# split exons that cross boundaries and get the
# nodes in the transcript path
for n in split_exons(t, boundaries):
n = (t.strand, n[0], n[1])
if is_ref:
node_dict[n].has_ref = True
else:
d = node_dict[n]
d.has_test = True
d.category = t.attrs['category']
splicing_pattern = []
for start,end in t.iterintrons():
n = (t.strand, start, end)
if is_ref:
intron_dict[n].has_ref = True
else:
d = intron_dict[n]
d.has_test = True
d.category = t.attrs['category']
splicing_pattern.append(n)
splicing_pattern = tuple(splicing_pattern)
if len(splicing_pattern) > 0:
if is_ref:
splicing_pattern_dict[splicing_pattern].has_ref = True
else:
d = splicing_pattern_dict[splicing_pattern]
d.has_test = True
d.category = t.attrs['category']
# handle unstranded transcripts
for t in unstranded_transcripts:
# separate ref and nonref transcripts
is_ref = bool(int(t.attrs[GTFAttr.REF]))
for n in split_exons(t, boundaries):
found_node = False
for strand in (POS_STRAND, NEG_STRAND):
sn = (strand, n[0], n[1])
if sn in node_dict:
if is_ref:
node_dict[sn].has_ref = True
else:
d = node_dict[sn]
d.has_test = True
d.category = t.attrs['category']
found_node = True
if not found_node:
sn = (NO_STRAND, n[0], n[1])
if is_ref:
node_dict[sn].has_ref = True
else:
d = node_dict[sn]
d.has_test = True
d.category = t.attrs['category']
introns = list(t.iterintrons())
assert len(introns) == 0
# compile statistics
for d in intron_dict.itervalues():
if d.has_ref and d.has_test:
self.introns_both += 1
self.introns_by_category[d.category] += 1
elif d.has_ref:
self.introns_ref_only += 1
elif d.has_test:
self.introns_test_only += 1
self.introns_by_category[d.category] += 1
for d in splicing_pattern_dict.itervalues():
if d.has_ref and d.has_test:
self.patterns_both += 1
self.patterns_by_category[d.category] += 1
elif d.has_ref:
self.patterns_ref_only += 1
elif d.has_test:
self.patterns_test_only += 1
self.patterns_by_category[d.category] += 1
for n,d in node_dict.iteritems():
strand, start, end = n
length = end - start
if d.has_ref and d.has_test:
self.cov_both += length
self.cov_by_category[d.category] += length
elif d.has_ref:
self.cov_ref_only += length
elif d.has_test:
self.cov_test_only += length
self.cov_by_category[d.category] += length
