def read_reference_gtf(ref_gtf_file):
gene_map = {}
for f in GTFFeature.parse(open(ref_gtf_file)):
# get gene by id
gene_id = f.attrs["gene_id"]
if gene_id not in gene_map:
g = Gene()
g.gene_id = gene_id
g.chrom = f.seqid
g.strand = f.strand
g.gene_start = f.start
g.gene_end = f.end
gene_map[gene_id] = g
else:
g = gene_map[gene_id]
# update gene
g.gene_start = min(g.gene_start, f.start)
g.gene_end = max(g.gene_end, f.end)
if f.feature_type == "exon":
g.exons.add((f.start, f.end))
elif f.feature_type == "CDS":
g.is_coding = True
if "gene_name" in f.attrs:
g.gene_names.add(f.attrs["gene_name"])
g.annotation_sources.add(f.source)
logging.info("Sorting genes")
genes = sorted(gene_map.values(),
key=operator.attrgetter('chrom', 'gene_start'))
del gene_map
# cluster loci
logging.debug("Building interval index")
locus_cluster_trees = collections.defaultdict(lambda: ClusterTree(0, 1))
locus_trees = collections.defaultdict(lambda: IntervalTree())
for i, g in enumerate(genes):
locus_cluster_trees[g.chrom].insert(g.gene_start, g.gene_end, i)
for chrom, cluster_tree in locus_cluster_trees.iteritems():
for locus_start, locus_end, indexes in cluster_tree.getregions():
# cluster gene exons and add to interval tree
exon_tree = IntervalTree()
for i in indexes:
g = genes[i]
cluster_tree = ClusterTree(0, 1)
for start, end in g.exons:
cluster_tree.insert(start, end, 1)
# update exons
exon_clusters = []
for start, end, indexes in cluster_tree.getregions():
exon_clusters.append((start, end))
g.exons = exon_clusters
del cluster_tree
for start, end in g.exons:
exon_tree.insert_interval(Interval(start, end, value=g))
# add to locus interval tree
locus_trees[chrom].insert_interval(
Interval(locus_start, locus_end, value=exon_tree))
logging.debug("Done indexing reference GTF file")
return locus_trees
def read_reference_gtf(ref_gtf_file):
gene_map = {}
for f in GTFFeature.parse(open(ref_gtf_file)):
# get gene by id
gene_id = f.attrs["gene_id"]
if gene_id not in gene_map:
g = Gene()
g.gene_id = gene_id
g.chrom = f.seqid
g.strand = f.strand
g.gene_start = f.start
g.gene_end = f.end
gene_map[gene_id] = g
else:
g = gene_map[gene_id]
# update gene
g.gene_start = min(g.gene_start, f.start)
g.gene_end = max(g.gene_end, f.end)
if f.feature_type == "exon":
g.exons.add((f.start, f.end))
elif f.feature_type == "CDS":
g.is_coding = True
if "gene_name" in f.attrs:
g.gene_names.add(f.attrs["gene_name"])
g.annotation_sources.add(f.source)
logging.info("Sorting genes")
genes = sorted(gene_map.values(), key=operator.attrgetter('chrom', 'gene_start'))
del gene_map
# cluster loci
logging.debug("Building interval index")
locus_cluster_trees = collections.defaultdict(lambda: ClusterTree(0,1))
for i,g in enumerate(genes):
locus_cluster_trees[g.chrom].insert(g.gene_start, g.gene_end, i)
locus_trees = collections.defaultdict(lambda: IntervalTree())
for chrom, cluster_tree in locus_cluster_trees.iteritems():
for locus_start,locus_end,indexes in cluster_tree.getregions():
# cluster gene exons and add to interval tree
exon_tree = IntervalTree()
for i in indexes:
g = genes[i]
cluster_tree = ClusterTree(0,1)
for start,end in g.exons:
cluster_tree.insert(start, end, 1)
# update exons
exon_clusters = []
for start,end,indexes in cluster_tree.getregions():
exon_clusters.append((start,end))
g.exons = exon_clusters
del cluster_tree
for start,end in g.exons:
exon_tree.insert_interval(Interval(start, end, value=g))
# add to locus interval tree
locus_trees[chrom].insert_interval(Interval(locus_start, locus_end, value=exon_tree))
logging.debug("Done indexing reference GTF file")
return locus_trees
def build_interval_tree_from_bed(bed_file):
trees = collections.defaultdict(lambda: IntervalTree())
for f in BEDFeature.parse(open(bed_file)):
tree = trees[f.chrom]
for start,end in f.exons:
tree.insert_interval(Interval(start, end, strand=f.strand, value=f.name))
return trees
def build_locus_trees(gtf_file):
transcripts = []
locus_cluster_trees = collections.defaultdict(lambda: ClusterTree(0,1))
for locus_transcripts in parse_gtf(open(gtf_file)):
for t in locus_transcripts:
is_ref = bool(int(t.attrs[GTFAttr.REF]))
if not is_ref:
continue
i = len(transcripts)
transcripts.append(t)
locus_cluster_trees[t.chrom].insert(t.start, t.end, i)
# build interval trees of loci
locus_trees = collections.defaultdict(lambda: IntervalTree())
for chrom, cluster_tree in locus_cluster_trees.iteritems():
for locus_start, locus_end, indexes in cluster_tree.getregions():
for i in indexes:
locus_transcripts = [transcripts[i] for i in indexes]
locus_trees[chrom].insert_interval(Interval(locus_start, locus_end, value=locus_transcripts))
return locus_trees
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 trim_graph(G, strand, min_trim_length, trim_utr_fraction,
trim_intron_fraction):
# get 'chains' of contiguous non-intron nodes with edge degree of
# one or less
node_chain_map, chains = get_chains(G, introns=False)
# setup dictionaries of predecessors and successors
successor_dict = {}
for n, nbrdict in G.adjacency_iter():
successor_dict[n] = nbrdict.keys()
predecessor_dict = {}
G.reverse(copy=False)
for n, nbrdict in G.adjacency_iter():
predecessor_dict[n] = nbrdict.keys()
G.reverse(copy=False)
# setup intron data structures
introns = {}
intron_tree = IntervalTree()
reverse = (strand == NEG_STRAND)
for u, nbrdict in G.adjacency_iter():
for v in nbrdict:
if reverse:
left, right = v, u
else:
left, right = u, v
# skip contiguous nodes
if left.end == right.start:
continue
# calculate score of the chains
u_chain_nodes = chains[node_chain_map[u]]
u_score = max(G.node[n][NODE_SCORE] for n in u_chain_nodes)
v_chain_nodes = chains[node_chain_map[v]]
v_score = max(G.node[n][NODE_SCORE] for n in v_chain_nodes)
# store scores in intron data structures
introns[(left.end, right.start)] = (u_score, v_score)
intron_tree.insert_interval(
Interval(left.end, right.start, value=(u_score, v_score)))
# trim chains
all_trim_nodes = set()
for parent, nodes in chains.iteritems():
if strand == NEG_STRAND:
nodes.reverse()
in_degree = len(predecessor_dict[nodes[0]])
out_degree = len(successor_dict[nodes[-1]])
trim_nodes = set()
if ((in_degree == 1) and (out_degree == 1)
and (parent.start, parent.end) in introns):
# intron retention - a chain of nodes precisely matches an
# intron, so we can potentially remove the entire chain
pred_score, succ_score = introns[(parent.start, parent.end)]
cutoff_score = trim_intron_fraction * max(pred_score, succ_score)
trim_nodes.update(trim_intron(G, nodes, cutoff_score))
else:
# determine whether this node chain is intronic. intronic node
# chains are trimmed more strictly due to intronic pre-mrna
found_intron = False
max_pred_score = 0.0
max_succ_score = 0.0
for hit in intron_tree.find(parent.start, parent.end):
# ignore contained introns
if (hit.start > parent.start) and (hit.end < parent.end):
continue
# set intron flag and keep track of highest coverage
# overlapping intron to make trimming conservative
found_intron = True
pred_score, succ_score = hit.value
if pred_score > max_pred_score:
max_pred_score = pred_score
if succ_score > max_succ_score:
max_succ_score = succ_score
if (in_degree == 0) and (out_degree == 0):
if found_intron:
cutoff_score = trim_intron_fraction * max(
max_pred_score, max_succ_score)
trim_nodes.update(trim_intron(G, nodes, cutoff_score))
trim_nodes.update(
trim_bidirectional(G, nodes, min_trim_length,
trim_utr_fraction))
elif in_degree == 0:
if found_intron:
cutoff_score = trim_intron_fraction * max_succ_score
trim_nodes.update(trim_intronic_utr(
G, nodes, cutoff_score))
trim_nodes.update(
trim_utr(G, nodes[::-1], min_trim_length,
trim_utr_fraction))
elif out_degree == 0:
if found_intron:
cutoff_score = trim_intron_fraction * max_pred_score
trim_nodes.update(
trim_intronic_utr(G, nodes[::-1], cutoff_score))
trim_nodes.update(
trim_utr(G, nodes, min_trim_length, trim_utr_fraction))
all_trim_nodes.update(trim_nodes)
if len(all_trim_nodes) > 0:
logging.debug("\t\t(%s) trimmed %d/%d nodes from graph" %
(strand_int_to_str(strand), len(all_trim_nodes), len(G)))
return all_trim_nodes
def trim_graph(G, strand,
min_trim_length,
trim_utr_fraction,
trim_intron_fraction):
# get 'chains' of contiguous non-intron nodes with edge degree of
# one or less
node_chain_map, chains = get_chains(G, introns=False)
# setup dictionaries of predecessors and successors
successor_dict = {}
for n,nbrdict in G.adjacency_iter():
successor_dict[n] = nbrdict.keys()
predecessor_dict = {}
G.reverse(copy=False)
for n,nbrdict in G.adjacency_iter():
predecessor_dict[n] = nbrdict.keys()
G.reverse(copy=False)
# setup intron data structures
introns = {}
intron_tree = IntervalTree()
reverse = (strand == NEG_STRAND)
for u,nbrdict in G.adjacency_iter():
for v in nbrdict:
if reverse:
left, right = v, u
else:
left, right = u, v
# skip contiguous nodes
if left.end == right.start:
continue
# calculate score of the chains
u_chain_nodes = chains[node_chain_map[u]]
u_score = max(G.node[n][NODE_SCORE] for n in u_chain_nodes)
v_chain_nodes = chains[node_chain_map[v]]
v_score = max(G.node[n][NODE_SCORE] for n in v_chain_nodes)
# store scores in intron data structures
introns[(left.end,right.start)] = (u_score, v_score)
intron_tree.insert_interval(Interval(left.end, right.start,
value=(u_score,v_score)))
# trim chains
all_trim_nodes = set()
for parent, nodes in chains.iteritems():
if strand == NEG_STRAND:
nodes.reverse()
in_degree = len(predecessor_dict[nodes[0]])
out_degree = len(successor_dict[nodes[-1]])
trim_nodes = set()
if ((in_degree == 1) and (out_degree == 1) and
(parent.start, parent.end) in introns):
# intron retention - a chain of nodes precisely matches an
# intron, so we can potentially remove the entire chain
pred_score, succ_score = introns[(parent.start, parent.end)]
cutoff_score = trim_intron_fraction * max(pred_score, succ_score)
trim_nodes.update(trim_intron(G, nodes, cutoff_score))
else:
# determine whether this node chain is intronic. intronic node
# chains are trimmed more strictly due to intronic pre-mrna
found_intron = False
max_pred_score = 0.0
max_succ_score = 0.0
for hit in intron_tree.find(parent.start, parent.end):
# ignore contained introns
if (hit.start > parent.start) and (hit.end < parent.end):
continue
# set intron flag and keep track of highest coverage
# overlapping intron to make trimming conservative
found_intron = True
pred_score, succ_score = hit.value
if pred_score > max_pred_score:
max_pred_score = pred_score
if succ_score > max_succ_score:
max_succ_score = succ_score
if (in_degree == 0) and (out_degree == 0):
if found_intron:
cutoff_score = trim_intron_fraction * max(max_pred_score, max_succ_score)
trim_nodes.update(trim_intron(G, nodes, cutoff_score))
trim_nodes.update(trim_bidirectional(G, nodes, min_trim_length, trim_utr_fraction))
elif in_degree == 0:
if found_intron:
cutoff_score = trim_intron_fraction * max_succ_score
trim_nodes.update(trim_intronic_utr(G, nodes, cutoff_score))
trim_nodes.update(trim_utr(G, nodes[::-1], min_trim_length, trim_utr_fraction))
elif out_degree == 0:
if found_intron:
cutoff_score = trim_intron_fraction * max_pred_score
trim_nodes.update(trim_intronic_utr(G, nodes[::-1], cutoff_score))
trim_nodes.update(trim_utr(G, nodes, min_trim_length, trim_utr_fraction))
all_trim_nodes.update(trim_nodes)
if len(all_trim_nodes) > 0:
logging.debug("\t\t(%s) trimmed %d/%d nodes from graph" %
(strand_int_to_str(strand), len(all_trim_nodes),
len(G)))
return all_trim_nodes
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 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)
