def simple_test():
graph = Graph({
1: Block(10),
2: Block(1),
3: Block(1),
4: Block(10)
}, {
1: [2, 3],
2: [4],
3: [4]
})
graph.convert_to_numpy_backend()
sequence_graph = SequenceGraph.create_empty_from_ob_graph(graph)
sequence_graph.set_sequence(1, "GGGTTTATAC")
sequence_graph.set_sequence(2, "A")
sequence_graph.set_sequence(3, "C")
sequence_graph.set_sequence(4, "GTACATTGTA")
linear_ref = Interval(0, 10, [1, 2, 3], graph)
linear_ref = linear_ref.to_numpy_indexed_interval()
critical_nodes = set([4])
finder = MinimizerFinder(graph,
sequence_graph,
critical_nodes,
linear_ref,
k=3,
w=3)
minimizers = finder.find_minimizers()
assert minimizers.has_minimizer(2, 0)
assert minimizers.has_minimizer(3, 0)
assert minimizers.has_minimizer(4, 4)
def setUp(self):
self.linear_graph = Graph({i: Block(5)
for i in range(1, 4)},
{i: [i + 1]
for i in range(1, 3)})
self.scores = DensePileup.from_intervals(
self.linear_graph, [Interval(0, 5, [i]) for i in range(1, 4)])
self.graph = Graph({i: Block(5)
for i in range(1, 4)}, {
1: [3],
2: [3],
3: [4]
})
def test_find_max_path_on_start_and_end_node(self):
graph = Graph({
1: Block(10),
2: Block(10),
3: Block(10),
4: Block(10)
}, {
1: [2, 3],
2: [4],
3: [4]
})
peak = ConnectedAreas(graph, {
2: [0, 10],
4: [0, 10],
})
binary_peak = BinaryContinousAreas.from_old_areas(peak)
qvalues = DensePileup.from_intervals(graph,
[Interval(7, 2, [1, 2, 4])])
scored_peak = ScoredPeak.from_peak_and_pileup(binary_peak, qvalues)
max_path = scored_peak.get_max_path()
self.assertEqual(max_path, Interval(0, 10, [2, 4]))
def test_find_max_path_through_subgraph_multiple_paths(self):
graph = Graph({
1: Block(10),
2: Block(10),
3: Block(10),
4: Block(10)
}, {
1: [2, 3],
2: [4],
3: [4]
})
peak = ConnectedAreas(graph, {
2: [0, 10],
3: [0, 10],
1: [5, 10],
4: [0, 3]
})
binary_peak = BinaryContinousAreas.from_old_areas(peak)
qvalues = DensePileup.from_intervals(
graph,
[
Interval(7, 2, [1, 3, 4]) # Giving higher qvalue
# through this path
])
print(qvalues)
scored_peak = ScoredPeak.from_peak_and_pileup(binary_peak, qvalues)
print(scored_peak)
max_path = scored_peak.get_max_path()
self.assertEqual(max_path, Interval(5, 3, [1, 3, 4]))
def test_create_from_nongraphpeakcollection(self):
graph = Graph({
1: Block(10),
2: Block(10),
3: Block(10)
}, {
1: [2],
2: [3]
})
graph.convert_to_numpy_backend()
linear_path = Interval(0, 10, [1, 2, 3], graph)
linear_path = linear_path.to_numpy_indexed_interval()
nongraph_peaks = NonGraphPeakCollection([
NonGraphPeak("chr1", 3, 10, 5),
NonGraphPeak("chr1", 13, 15, 7),
])
peaks = PeakCollection.create_from_nongraph_peak_collection(
graph, nongraph_peaks, linear_path, None)
self.assertEqual(peaks.intervals[0], Interval(3, 10, [1]))
self.assertEqual(peaks.intervals[1], Interval(3, 5, [2]))
peaks = PeakCollection.create_from_nongraph_peak_collection(
graph, nongraph_peaks, linear_path, LinearRegion("chr1", 3, 20))
self.assertEqual(peaks.intervals[0], Interval(0, 7, [1]))
self.assertEqual(peaks.intervals[1], Interval(0, 2, [2]))
def test_three_nodes_in(self):
graph = Graph({i: Block(5)
for i in range(1, 5)}, {
1: [4],
2: [4],
3: [4]
})
intervals = [
Interval(2, 5, [1]),
Interval(2, 5, [2]),
Interval(2, 5, [3]),
Interval(0, 3, [4])
]
pileup = DensePileup.from_intervals(graph, intervals)
subgraphs = SubgraphCollectionPartiallyOrderedGraph.create_from_pileup(
graph, pileup)
print(subgraphs)
correct1 = BinaryContinousAreas(graph)
correct1.add_start(-1, 3)
correct1.add_start(-2, 3)
correct1.add_start(-3, 3)
correct1.add_start(4, 3)
self.assertTrue(correct1 in subgraphs)
def test_simple3(self):
graph = Graph({i: Block(5)
for i in range(1, 6)}, {
1: [3],
2: [3],
3: [4, 5]
})
scores = DensePileup.from_intervals(
graph, [Interval(0, 5, [i]) for i in range(1, 6)])
intervals = [
Interval(0, 5, [1]),
Interval(0, 5, [3]),
Interval(0, 5, [4]),
Interval(0, 3, [5])
]
pileup = DensePileup.from_intervals(graph, intervals)
subgraphs = SubgraphCollectionPartiallyOrderedGraph.create_from_pileup(
graph, pileup)
scored_peaks = (ScoredPeak.from_peak_and_pileup(peak, scores)
for peak in subgraphs)
max_paths = [peak.get_max_path() for peak in scored_peaks]
self.assertTrue(
Interval(0, 5, [1, 3, 4]) in max_paths
or Interval(0, 3, [1, 3, 5]) in max_paths)
def __init__(self, tf_experiment_dir, data_dir):
self.experiment_dir = tf_experiment_dir
self.data_dir = data_dir
self.bam_file = pysam.AlignmentFile(self.experiment_dir + "/linear_alignments.bam", "rb")
self.linear_path = NumpyIndexedInterval.from_file(self.data_dir + "/5_linear_pathv2.interval")
self.graph = Graph.from_file(self.data_dir + "/5.nobg")
self.alignment_collection = AlignmentCollection.from_file(self.experiment_dir + "/5_alignments.pickle", self.graph)
self.check_peaks()
def test_reverse():
graph = Graph({
1: Block(10),
2: Block(5),
3: Block(10),
4: Block(5)
}, {
1: [2, 3],
2: [4],
3: [4]
})
graph.convert_to_numpy_backend()
linear_path = NumpyIndexedInterval.from_interval(
Interval(0, 10, [1, 2, 4], graph))
alignments = [Interval(4, 5, [-3, -1], graph)]
projected = project_alignments(alignments, linear_path)
projected = list(projected)
assert projected[0] == (5, 16, "-")
def set_graph(self):
self.graph = Graph({
1: Block(5),
2: Block(5),
3: Block(5)
}, {
1: [2],
2: [3]
})
def test_simple():
graph = Graph({
1: Block(10),
2: Block(5),
3: Block(10),
4: Block(5)
}, {
1: [2, 3],
2: [4],
3: [4]
})
graph.convert_to_numpy_backend()
linear_path = NumpyIndexedInterval.from_interval(
Interval(0, 10, [1, 2, 4], graph))
alignments = [Interval(5, 5, [1, 3], graph), Interval(5, 5, [3, 4], graph)]
projected = project_alignments(alignments, linear_path)
projected = list(projected)
assert projected[0] == (5, 15, "+")
assert projected[1] == (15, 25, "+")
def setUp(self):
self.graph = Graph({i: Block(3)
for i in range(1, 7)},
{i: [i + 1]
for i in range(1, 6)})
self.peaks = PeakCollection([
Peak(3, 3, [1, 2, 3, 4], self.graph),
Peak(3, 3, [5, 6], self.graph)
])
def test_many_nodes():
nodes = {i: Block(1) for i in range(2, 10)}
nodes[1] = Block(10)
nodes[10] = Block(10)
graph = Graph(
nodes, {
1: [2, 3],
2: [4],
3: [4],
4: [5, 6],
5: [7],
6: [7],
7: [8, 9],
8: [10],
9: [10]
})
graph.convert_to_numpy_backend()
sequence_graph = SequenceGraph.create_empty_from_ob_graph(graph)
sequence_graph.set_sequence(1, "ACTGACTGAC")
sequence_graph.set_sequence(10, "ACTGACTGAC")
sequence_graph.set_sequence(2, "A")
sequence_graph.set_sequence(3, "C")
sequence_graph.set_sequence(4, "A")
sequence_graph.set_sequence(5, "G")
sequence_graph.set_sequence(6, "C")
sequence_graph.set_sequence(7, "T")
sequence_graph.set_sequence(8, "A")
sequence_graph.set_sequence(9, "A")
linear_ref = Interval(0, 10, [1, 2, 4, 6, 7, 8, 10], graph)
linear_ref = linear_ref.to_numpy_indexed_interval()
critical_nodes = {1, 4, 7, 10}
finder = MinimizerFinder(graph,
sequence_graph,
critical_nodes,
linear_ref,
k=3,
w=3)
minimizers = finder.find_minimizers()
print(len(minimizers.minimizers))
def setUp(self):
self.simple_graph = Graph({i: Block(3)
for i in range(1, 9)}, {
1: [2, 3],
2: [4],
3: [4],
4: [5],
5: [6, 7],
6: [8],
7: [8]
})
print(self.simple_graph.get_first_blocks())
print(self.simple_graph.reverse_adj_list)
self.simple_snarls = \
{
20: SimpleSnarl(1, 4, 20),
21: SimpleSnarl(5, 8, 21),
22: SimpleSnarl(4, 5, 22)
}
def set_graph(self):
self.graph = Graph({
1: Block(5),
2: Block(5),
3: Block(5),
4: Block(5)
}, {
1: [2, 3],
2: [4],
3: [4]
})
def macs_to_graph_peaks(folder):
for chrom in ["1", "2", "3", "4", "5"]:
path = NumpyIndexedInterval.from_file("/data/bioinf/tair2/" + chrom +
"_linear_pathv2.interval")
graph = Graph.from_file("/data/bioinf/tair2/" + chrom + ".nobg")
macs_peaks = PeakCollection.from_fasta_file(
folder + "/macs_sequences_chr%s_summits_unique.fasta" % chrom,
graph)
macs_peaks.to_file(
folder +
"/%s_macs_unique_graph_summits.intervalcollection" % chrom, True)
def test_overlapping_alt_loci(self):
chrom_file = "data/chrom.sizes.test"
alt_loci = "data/alt_loci_test"
graph = create_initial_grch38_graph(chrom_file)
numeric_graph, name_translation = convert_to_numeric_graph(graph)
self.assertEqual(len(graph.blocks), 3)
self.assertEqual(len([a for a, v in graph.adj_list.items() if v]), 0)
new_numeric_graph, numeric_translation = \
connect_without_flanks(numeric_graph, alt_loci, name_translation)
correct_graph_structure = Graph(
{
1: Block(1),
2: Block(1),
3: Block(1),
4: Block(1),
5: Block(1),
6: Block(1),
7: Block(1),
8: Block(1),
9: Block(1),
},
{
1: [2, 8],
2: [3, 9],
3: [4],
4: [5],
5: [6],
6: [7],
9: [5],
8: [6]
}
)
self.assertTrue(correct_graph_structure.has_identical_structure(new_numeric_graph))
def run_predict_path(args):
chromosomes = args.chromosomes.split(",")
processes = []
if not os.path.isfile(args.alignments):
logging.error("Input alignments file %s does not exist" %
args.alignments)
sys.exit()
for chromosome in chromosomes:
logging.info("Starting process for chromosome %s " % chromosome)
process = Process(target=run_predict_path_single_chromosome,
args=(args.alignments, chromosome, args.data_dir,
args.linear_ref_bonus, args.out_file_name,
args.max_nodes_to_traverse))
process.start()
processes.append(process)
for process in processes:
process.join()
# Merge all fasta files that were produces
out_fasta = open(args.out_file_name + ".fa", "w")
logging.info("Merging fasta files")
for chromosome in tqdm(chromosomes):
with open(args.out_file_name + "_" + chromosome + ".fasta") as f:
out_fasta.write(f.read())
logging.info("Wrote resulting linear reference to %s" %
(args.out_file_name + ".fa"))
# Create indexed intervals for each interval file that was produced
logging.info("Creating indexed interval for all chromosomes")
for chromosome in chromosomes:
file_name = args.out_file_name + "_" + chromosome + ".intervalcollection"
graph = Graph.from_file(args.data_dir + chromosome + ".nobg")
intervals = IntervalCollection.from_file(file_name,
text_file=True,
graph=graph)
intervals = list(intervals.intervals)
assert len(
intervals) == 1, "Only a single interval in file is supported"
interval = intervals[0]
indexed = interval.to_numpy_indexed_interval()
indexed.to_file(file_name + ".indexed")
logging.info("Wrote indexed interval to file %s" % file_name +
".indexed")
if not args.skip_bwa_index:
logging.info("Running bwa index")
run_bwa_index(args.out_file_name + ".fa")
else:
logging.info("Not creating bwa index")
def test_many_nodes():
nodes = {i: Block(1) for i in range(2, 10)}
nodes[1] = Block(10)
nodes[10] = Block(10)
graph = Graph(
nodes, {
1: [2, 3],
2: [4],
3: [4],
4: [5, 6],
5: [7],
6: [7],
7: [8, 9],
8: [10],
9: [10]
})
graph.convert_to_numpy_backend()
sequence_graph = SequenceGraph.create_empty_from_ob_graph(graph)
sequence_graph.set_sequence(1, "ACTGACTGAC")
sequence_graph.set_sequence(10, "ACTGACTGAC")
sequence_graph.set_sequence(2, "A")
sequence_graph.set_sequence(3, "C")
sequence_graph.set_sequence(4, "A")
sequence_graph.set_sequence(5, "G")
sequence_graph.set_sequence(6, "C")
sequence_graph.set_sequence(7, "T")
sequence_graph.set_sequence(8, "T")
sequence_graph.set_sequence(9, "A")
linear_ref_nodes = {1, 2, 4, 6, 7, 8, 10}
read_sequence = "ACTGACCAGTAACTGAC"
start_node = 1
start_offset = 4
aligner = LocalGraphAligner(graph, sequence_graph, read_sequence,
linear_ref_nodes, start_node, start_offset)
alignment, score = aligner.align()
assert alignment == [1, 3, 4, 5, 7, 9, 10]
def visualize_alt_locus(args, skip_wrapping=False, quiet=False):
from offsetbasedgraph.graphutils import GeneList, \
create_gene_dicts, create_subgraph_around_alt_locus
if not isinstance(args.translation_file_name, Translation):
trans = Translation.from_file(args.translation_file_name)
else:
trans = args.translation_file_name
graph = trans.graph2
orig_trans = trans.copy()
# Find all genes on this graph
genes = GeneList(get_gene_objects_as_intervals(args.genes)).gene_list
alt_loci_genes, gene_name_dict, main_genes = create_gene_dicts(genes, alt_loci_fn=args.alt_locations_file_name)
genes = main_genes[args.alt_locus] + alt_loci_genes[args.alt_locus]
genes = [g.translate(trans) for g in genes]
subgraph, trans, start_position = create_subgraph_around_alt_locus(graph, trans, args.alt_locus, 200000, alt_loci_fn=args.alt_locations_file_name)
start_position = orig_trans.translate_position(start_position, True)[0]
genes = [g for g in genes if not g.multiple_alt_loci() and g.transcription_region.length() > 100]
if len(genes) > 40:
genes = genes[0:40]
levels = Graph.level_dict(subgraph.blocks)
# Find start block by choosing a block having no edges in
start = None
for b in subgraph.blocks:
if len(subgraph.reverse_adj_list[b]) == 0:
start = b
break
assert start is not None
from visualizehtml import VisualizeHtml
subgraph.start_block = start
max_offset = sum([subgraph.blocks[b].length() for b in subgraph.blocks])
v = VisualizeHtml(subgraph, 0, max_offset, 0, levels, "", 800, genes, start_position)
if quiet:
return
if skip_wrapping:
print(str(v))
else:
print(v.get_wrapped_html())
def setUp(self):
self.graph = Graph({i: Block(10)
for i in range(1, 4)},
{i: [i + 1]
for i in range(1, 3)})
self.index = GraphIndex({
1: [(2, 10), (3, 20)],
2: [(3, 10)],
3: [],
-1: [],
-2: [(-1, 10)],
-3: [(-2, 10), (-1, 20)]
})
self.extender = GraphExtender(self.index)
def setUp(self):
self.complex_graph = Graph(
{i: Block(3) for i in range(1, 13)},
{
1: [2, 3],
2: [7, 8],
3: [4, 5],
4: [6],
5: [6],
6: [10],
7: [9],
8: [9],
9: [10],
10: [12]
})
self.complex_graph.convert_to_numpy_backend()
def test_find_max_path_through_subgraph_two_node_graph(self):
graph = Graph({1: Block(10), 2: Block(10)}, {1: [2]})
peak = ConnectedAreas(graph, {2: [0, 4], 1: [5, 10]})
binary_peak = BinaryContinousAreas.from_old_areas(peak)
qvalues = DensePileup.from_base_value(graph, 10)
print("q values")
print(qvalues)
print(qvalues.data._values)
scored_peak = ScoredPeak.from_peak_and_pileup(binary_peak, qvalues)
print(scored_peak)
max_path = scored_peak.get_max_path()
self.assertEqual(max_path, Interval(5, 4, [1, 2]))
def run_predict_path_single_chromosome(alignment_file_name, chromosome,
graph_dir, linear_ref_bonus,
out_file_base_name,
max_nodes_to_traverse):
sequence_graph = SequenceGraph.from_file(graph_dir + chromosome +
".nobg.sequences")
graph = Graph.from_file(graph_dir + chromosome + ".nobg")
linear_path = NumpyIndexedInterval.from_file(graph_dir +
"/%s_linear_pathv2.interval" %
chromosome)
PathPredicter(alignment_file_name,
graph,
sequence_graph,
chromosome,
linear_path,
out_file_base_name,
linear_ref_bonus=linear_ref_bonus,
max_nodes_to_traverse=max_nodes_to_traverse)
def read_graphs(graph_dir, chromosomes):
logging.info("Reading graphs")
graphs = {}
sequence_graphs = {}
linear_ref_nodes = {}
for chromosome in chromosomes:
chromosome_name = chromosome
if chromosome == "X":
chromosome_name = "23"
logging.info("Reading graphs for chromosome %s" % chromosome)
graphs[chromosome_name] = Graph.from_file(graph_dir + chromosome +
".nobg")
sequence_graphs[chromosome_name] = SequenceGraph.from_file(
graph_dir + chromosome + ".nobg.sequencesv2")
linear_ref_nodes[
chromosome_name] = None #NumpyIndexedInterval.from_file(graph_dir + chromosome + "_linear_pathv2.interval").nodes_in_interval()
return graphs, sequence_graphs, linear_ref_nodes
def setUp(self):
self.graph = Graph({i: Block(10)
for i in range(1, 5)}, {
1: [2, 3],
2: [4],
3: [4]
})
self.index = GraphIndex({
1: [(2, 10), (3, 10), (4, 20)],
2: [(4, 10)],
3: [(4, 10)],
4: [],
-1: [],
-2: [(-1, 10)],
-3: [(-1, 10)],
-4: [(-2, 10), (-3, 10), (-1, 20)]
})
self.extender = GraphExtender(self.index)
def _coordinate(self, rp):
"""
Returns the hierarhcial and sequential coordinates of a region path
"""
length = self.graph.blocks[rp].length()
# Translate rp back to get GRCh38 hier. coordinates
from offsetbasedgraph import Interval, Graph
hier_id = str(rp)
hier_of = 0
origin = Graph.block_origin(rp)
if origin == "main" or origin == "merged":
dist_back = self._distance_to_start(rp)
hier_id = self.start_position.region_path_id
hier_of = dist_back + self.start_position.offset
return (str(rp), "0", str(hier_id), str(hier_of), str(length))
def test_find_max_path_through_subgraph_with_illegal_paths(self):
graph = Graph(
{
1: Block(10),
2: Block(10),
3: Block(10),
4: Block(10)
},
{
1: [2, 3],
2: [4],
-4: [-3] # Making 3=>4 not allowed path
})
peak = ConnectedAreas(graph, {
2: [0, 10],
3: [0, 10],
1: [5, 10],
4: [0, 8]
})
binary_peak = BinaryContinousAreas.from_old_areas(peak)
qvalues = DensePileup.from_intervals(
graph,
[
Interval(0, 10, [3]), # Higher value on 3 than 2
Interval(0, 10, [3]),
Interval(0, 10, [4]), # Highest value if ending on 4
Interval(0, 10, [4]),
Interval(0, 10, [1]), # Highest value if inncluding 1
Interval(0, 10, [1]), # Highest value if inncluding 1
Interval(0, 10, [1, 2, 4])
])
scored_peak = ScoredPeak.from_peak_and_pileup(binary_peak, qvalues)
max_path = scored_peak.get_max_path()
print(max_path)
self.assertEqual(max_path, Interval(5, 8, [1, 2, 4]))
def _create_data(self):
node_offset = 1
for chrom_number, chromosome in enumerate(self.chromosomes):
graph = Graph(
{i + node_offset: Block(10)
for i in range(0, 3)},
{i + node_offset: [i + 1 + node_offset]
for i in range(0, 2)})
linear_map = LinearMap.from_graph(graph)
linear_map_file_name = "linear_map_%s.npz" % chromosome
linear_map.to_file(linear_map_file_name)
self.linear_maps.append(linear_map_file_name)
self.sequence_retrievers.append(
SequenceRetriever(
{i + node_offset: "A" * 10
for i in range(0, 3)}))
self._create_reads(chrom_number, chromosome, graph)
node_offset += 3
graph.convert_to_numpy_backend()
SequenceGraph.create_empty_from_ob_graph(graph).to_file(
chromosome + ".nobg.sequences")
graph.to_file(chromosome + ".nobg")
def test_convert_to_approx_linear_peaks(self):
graph = Graph({i: Block(3)
for i in range(1, 10)}, {
1: [2],
2: [3],
3: [4],
4: [5],
5: [6],
6: [7, 8],
7: [9],
9: [9]
})
graph.convert_to_numpy_backend()
linear_interval = Interval(0, 3, [2, 4, 8, 9], graph)
linear_interval = linear_interval.to_numpy_indexed_interval()
peaks = PeakCollection([Peak(2, 2, [2, 3, 4]), Peak(1, 1, [3, 4, 5])])
linear_peaks = peaks.to_approx_linear_peaks(linear_interval, "chr4")
linear_peaks = linear_peaks.peaks
print(linear_peaks)
self.assertEqual(linear_peaks[0], NonGraphPeak("chr4", 2, 5))
self.assertEqual(linear_peaks[1], NonGraphPeak("chr4", 3, 3))
def test_simple(self):
graph = Graph(
{i: Block(3) for i in range(1, 5)},
{
1: [2, 3],
2: [4],
3: [4]
}
)
graph.convert_to_numpy_backend()
intervals = IntervalCollection([
Interval(0, 3, [1, 3])
])
haplotyper = HaploTyper(graph, intervals)
haplotyper.build()
max_interval = haplotyper.get_maximum_interval_through_graph()
self.assertEqual(
max_interval,
Interval(0, 3, [1, 3, 4])
)
def make_haplotype_paths(graph_file_name, linear_ref_path_file_name,
haplotype0_file_name, haplotype1_file_name,
out_base_name, chromosome):
# Make a linear reference fasta and interval and haplotypes fasta and intervals
chrom = chromosome
graph = Graph.from_file(graph_file_name)
sequence_graph = SequenceGraph.from_file(graph_file_name + ".sequences")
linear_ref = IntervalCollection.from_file(linear_ref_path_file_name,
text_file=True)
linear_ref = list(linear_ref.intervals)[0]
linear_ref_nodes = set(linear_ref.region_paths)
# Write linear ref fasta to file
linear_ref_seq = sequence_graph.get_interval_sequence(linear_ref)
out_file = open("linear_ref_" + chrom + ".fasta", "w")
out_file.writelines([">%s\n" % chrom])
out_file.writelines([linear_ref_seq + "\n"])
out_file.close()
logging.info("Wrote linear ref sequence. N nodes in linear ref: %d" %
len(linear_ref_nodes))
haplotype_nodes = [set(), set()] # For haplotype 0 and 1
for haplotype in [0, 1]:
haplotype_file_name = haplotype0_file_name
if haplotype == 1:
haplotype_file_name = haplotype1_file_name
intervals = vg_json_file_to_intervals(haplotype_file_name, graph)
for interval in intervals:
for node in interval.region_paths:
haplotype_nodes[haplotype].add(node)
logging.info("N nodes in haplotype 0: %d" % len(haplotype_nodes[0]))
logging.info("N nodes in haplotype 0 that are also in linear ref: %d" %
len(haplotype_nodes[0].intersection(linear_ref_nodes)))
logging.info("N nodes in haplotype 1: %d" % len(haplotype_nodes[1]))
# Traverse graph to get full correct haplotype intervals
first_nodes = graph.get_first_blocks()
assert len(first_nodes) == 1
logging.info("N nodes in graph: %d" % len(graph.blocks))
for haplotype in [0, 1]:
logging.info("Traversing haplotype %d" % haplotype)
nodes = []
node = first_nodes[0]
nodes_in_haplotype = haplotype_nodes[haplotype]
nodes_in_haplotype = set(range(
0, max(linear_ref_nodes))).difference(linear_ref_nodes)
logging.info("There are %d haplotype nodes" % len(nodes_in_haplotype))
assert len(
nodes_in_haplotype
) > 0, "There are no haplotype nodes. Check that haplotype json files are not empty"
n_haplotype_nodes = 0
i = 0
while True:
nodes.append(node)
if i % 50000 == 0:
logging.info("#%d nodes traversed. On node %d" % (i, node))
i += 1
next_nodes = set(graph.adj_list[node])
if len(next_nodes) == 0:
logging.info("Reached end node %d with 0 edges" % node)
break
next_on_haplotype = next_nodes.intersection(nodes_in_haplotype)
if len(next_on_haplotype) == 1:
n_haplotype_nodes += 1
next_node = list(next_on_haplotype)[0]
assert next_node != node
node = next_node
elif len(next_on_haplotype) == 0:
logging.debug(
"No new haplotype node from %d. Will follow reference" %
node)
# Choose reference with lowest id to avoid deletion
node = min(list(next_nodes.intersection(linear_ref_nodes)))
else:
# logging.warning("There is a deletion from node %d. Choosing lowest node id as next to avoid deletion." % node)
# This means more than one next node is on haplotype. Choose the one with lowest id to avoid taking deletion
node = min(list(next_on_haplotype))
logging.info("Found %d nodes. %d on haplotype" %
(len(nodes), n_haplotype_nodes))
haplotype_interval = Interval(0, graph.blocks[nodes[-1]].length(),
nodes, graph)
print("Path length: %d" % haplotype_interval.length())
file_base_name = out_base_name + "_" + str(haplotype)
IntervalCollection([haplotype_interval
]).to_file(file_base_name + ".intervalcollection",
text_file=True)
sequence = sequence_graph.get_interval_sequence(haplotype_interval)
out_file = open(file_base_name + ".fasta", "w")
out_file.writelines([">%s\n" % chrom])
out_file.writelines([sequence + "\n"])
out_file.close()
logging.info("Wrote fasta sequence to %s" % file_base_name + ".fasta")
def count_variants_in_graph(graph, linear_path):
reference_nodes = linear_path.nodes_in_interval()
n_variants = 0
i = 0
for node in graph.blocks:
if i % 1000000 == 0:
print("Node #%d" % i)
i += 1
if node not in reference_nodes:
continue
n_variants += max(0, len(graph.adj_list[node]) - 1)
print("Variants: %d" % n_variants)
return n_variants
if __name__ == "__main__":
n_variants = 0
for chromosome in sys.argv[2].split(","):
print("Chromosome %s" % chromosome)
graph = Graph.from_file(sys.argv[1] + "/" + chromosome +
"_pruned.nobg")
linear_path = NumpyIndexedInterval.from_file(sys.argv[1] + "/" +
chromosome +
"_linear_pathv2.interval")
n_variants += count_variants_in_graph(graph, linear_path)
print("Total: %d" % n_variants)