def test_filter_fimo_results(self):
fimo_dir = os.path.join(self.options.output_dir, "fimo")
motifs = [Motif(0, 3)]
# gene2 will be the anchor promoter
anchor_promoter = 1
promoters = []
for i in range(1, 16):
promoters.append(Promoter("gene%d" % i, i * 10, i * 10 + 4))
# need certain amount of promoters, otherwise the proportion of
# promoters with a motif (motif frequency) will be too high --> error
expected_motifs = [Motif(0, 3, hits={"gene1": 1, "gene2": 2})]
# fake FIMO output file, corresponding to expected_motifs
source = path.get_full_path(__file__, "data", "fake_short_fimo.txt")
target = os.path.join(fimo_dir, "+00_-03")
if not os.path.exists(target):
os.makedirs(target)
copy(source, os.path.join(target,
"fimo.txt")) # overwrite fimo.txt if exists
found_motifs = filter_fimo_results(motifs, fimo_dir, promoters,
anchor_promoter)
assert found_motifs == expected_motifs
bs_per_promoter, expected_bs_per_promoter = read_generated_expected_file(
os.path.join(target, "bs_per_promoter.csv"),
"expected_bs_per_promoter.csv")
self.assertEqual(bs_per_promoter, expected_bs_per_promoter)
def test_regeneration(self):
record = create_fake_record()
results = cassis.CassisResults(record.id)
# create a prediction, since it will generate a border with many extra qualifiers
start_marker = ClusterMarker("gene1", Motif(3, 3, score=1))
start_marker.promoter = "gene1"
start_marker.abundance = 2
end_marker = ClusterMarker("gene4", Motif(3, 3, score=1))
end_marker.promoter = "gene3+gene4"
assert end_marker.abundance == 1
cluster = cassis.ClusterPrediction(start_marker, end_marker)
results.subregions = cassis.create_subregions("gene1", [cluster],
record)
assert results.subregions
results.promoters = [
Promoter("gene1", 10, 20, seq=Seq("cgtacgtacgt")),
Promoter("gene2", 30, 40, seq=Seq("cgtacgtacgt")),
CombinedPromoter("gene3", "gene4", 50, 60, seq=Seq("cgtacgtacgt"))
]
round_trip = cassis.regenerate_previous_results(
results.to_json(), record, None)
assert isinstance(round_trip, cassis.CassisResults)
assert len(results.subregions) == len(round_trip.subregions)
for old, new in zip(results.subregions, round_trip.subregions):
assert old.location == new.location
assert old.to_biopython()[0].qualifiers == new.to_biopython(
)[0].qualifiers
assert round_trip.promoters == results.promoters
def test_conversion(self):
motif = Motif(3, 3)
assert motif.pairing_string == "+03_-03"
motif.plus = 4
assert motif.pairing_string == "+04_-03"
motif.minus = 2
assert motif.pairing_string == "+04_-02"
def test_filter_meme_results(self):
meme_dir = os.path.join(self.options.output_dir, "meme")
anchor = "AFUA_6G09660"
promoter_sets = [Motif(0, 3)]
motif = Motif(0, 3, score=3.9e+003)
motif.seqs = [
"TTTCGACCCGTC", "TTTCAAACCGTC", "TTTTGATTCGTC", "TTTTGACCGGTC",
"TTTTAGACGGTC", "TTTTACCTCGTC", "TCTCGATCCGTC", "TTTCTATCCGTT",
"TTTTGGACCGCC", "ATTTGGCCTGTC", "TGTTGTCTCGTC", "TTTGAGGCCGTC",
"TTGTATTCTGTC", "TTTCTTCCTGTT"
]
expected_motifs = [motif]
# this is a "real" MEME output file, I was too lazy to create my own fake XML file
source = path.get_full_path(__file__, "data", "real_meme.xml")
target = os.path.join(meme_dir, "+00_-03")
if not os.path.exists(target):
os.makedirs(target)
copy(source, os.path.join(target,
"meme.xml")) # overwrite meme.xml if exists
self.assertEqual(
list(filter_meme_results(meme_dir, promoter_sets, anchor)),
expected_motifs)
binding_sites, expected_binding_sites = read_generated_expected_file(
os.path.join(meme_dir, "+00_-03", "binding_sites.fasta"),
"expected_binding_sites.fasta")
self.assertEqual(binding_sites, expected_binding_sites)
def test_check_cluster_predictions(self):
seq_record = create_fake_record()
promoters = [
Promoter("gene1", 1, 5),
Promoter("gene2", 6, 10),
CombinedPromoter("gene3", "gene4", 11, 15)
]
ignored_genes = [ # see captured logging
Gene(FeatureLocation(1, 5), locus_tag="gene5")
]
clusters = [
ClusterPrediction(ClusterMarker("gene1", Motif(3, 3, score=1)),
ClusterMarker("gene4", Motif(3, 3, score=1)))
]
expected = [
ClusterPrediction(ClusterMarker("gene1", Motif(3, 3, score=1)),
ClusterMarker("gene4", Motif(3, 3, score=1)))
]
expected[0].start.promoter = "gene1"
expected[0].end.promoter = "gene3+gene4"
expected[0].genes = 4
expected[0].promoters = 3
assert check_cluster_predictions(clusters, seq_record, promoters,
ignored_genes) == expected
def test_cleanup_outdir(self):
anchor_genes = ["gene1", "gene4"]
cluster = cassis.ClusterPrediction(
ClusterMarker("gene1", Motif(3, 3, score=1)),
ClusterMarker("gene4", Motif(3, 3, score=1)))
cluster.start.promoter = "gene1"
cluster.end.promoter = "gene3+gene4"
cluster.genes = 4
cluster.promoters = 3
cluster_predictions = {"gene1": [cluster]}
# create some empty test dirs, which should be deleted during the test
# prediction! --> keep!
os.makedirs(
os.path.join(self.options.output_dir, "meme", "gene1", "+03_-03"))
# prediction! --> keep!
os.makedirs(
os.path.join(self.options.output_dir, "fimo", "gene1", "+03_-03"))
# no prediction --> delete
os.makedirs(
os.path.join(self.options.output_dir, "meme", "gene1", "+04_-04"))
# no prediction --> delete
os.makedirs(
os.path.join(self.options.output_dir, "fimo", "gene1", "+04_-04"))
# no prediction --> delete
os.makedirs(
os.path.join(self.options.output_dir, "meme", "gene4", "+03_-03"))
# no prediction --> delete
os.makedirs(
os.path.join(self.options.output_dir, "fimo", "gene4", "+03_-03"))
# prediction for this gene, but not from this motif --> delete
os.makedirs(
os.path.join(self.options.output_dir, "meme", "gene4", "+04_-04"))
# prediction for this gene, but not from this motif --> delete
os.makedirs(
os.path.join(self.options.output_dir, "fimo", "gene4", "+04_-04"))
cassis.cleanup_outdir(anchor_genes, cluster_predictions, self.options)
# assert kept directories
self.assertTrue("gene1" in os.listdir(
os.path.join(self.options.output_dir, "meme")))
self.assertTrue("gene1" in os.listdir(
os.path.join(self.options.output_dir, "fimo")))
self.assertTrue("+03_-03" in os.listdir(
os.path.join(self.options.output_dir, "meme", "gene1")))
self.assertTrue("+03_-03" in os.listdir(
os.path.join(self.options.output_dir, "fimo", "gene1")))
# assert deleted directories
self.assertTrue("gene4" not in os.listdir(
os.path.join(self.options.output_dir, "meme")))
self.assertTrue("gene4" not in os.listdir(
os.path.join(self.options.output_dir, "fimo")))
self.assertTrue("+04_-04" not in os.listdir(
os.path.join(self.options.output_dir, "meme", "gene1")))
self.assertTrue("+04_-04" not in os.listdir(
os.path.join(self.options.output_dir, "fimo", "gene1")))
def test_store_clusters(self):
# this test is similar to test_store_promoters
anchor = "gene3"
start_marker = ClusterMarker("gene1", Motif(3, 3, score=1))
start_marker.promoter = "gene1"
start_marker.abundance = 2
end_marker = ClusterMarker("gene4", Motif(3, 3, score=1))
end_marker.promoter = "gene3+gene4"
assert end_marker.abundance == 1
first_cluster = cassis.ClusterPrediction(start_marker, end_marker)
first_cluster.promoters = 3
first_cluster.genes = 4
start_marker = ClusterMarker("gene1", Motif(4, 4, score=1))
start_marker.promoter = "gene1"
assert start_marker.abundance == 1
end_marker = ClusterMarker("gene5", Motif(4, 4, score=1))
end_marker.promoter = "gene5"
assert end_marker.abundance == 1
second_cluster = cassis.ClusterPrediction(start_marker, end_marker)
second_cluster.promoters = 3
second_cluster.genes = 4
clusters = [first_cluster, second_cluster]
record_with_clusters = create_fake_record()
record_without_clusters = create_fake_record(
) # just the same, without adding clusters
borders = cassis.create_cluster_borders(anchor, clusters,
record_with_clusters)
assert record_with_clusters.get_feature_count(
) == record_without_clusters.get_feature_count()
for border in borders:
record_with_clusters.add_cluster_border(border)
# test if store_clusters changed any non-cluster feature (should not!) # TODO
# test cluster features
assert record_without_clusters.get_feature_count() + len(
clusters) == record_with_clusters.get_feature_count()
for i, cluster in enumerate(clusters):
cluster_border = record_with_clusters.get_cluster_borders()[i]
self.assertEqual(cluster_border.type, "cluster_border")
self.assertEqual(cluster_border.tool, "cassis")
self.assertEqual(cluster_border.get_qualifier("anchor"),
(anchor, ))
self.assertEqual(cluster_border.get_qualifier("genes"),
(cluster.genes, ))
self.assertEqual(cluster_border.get_qualifier("promoters"),
(cluster.promoters, ))
self.assertEqual(cluster_border.get_qualifier("gene_left"),
(cluster.start.gene, ))
self.assertEqual(cluster_border.get_qualifier("gene_right"),
(cluster.end.gene, ))
def test_store_subregions(self):
# this test is similar to test_store_promoters
anchor = "gene3"
start_marker = ClusterMarker("gene1", Motif(3, 3, score=1))
start_marker.promoter = "gene1"
start_marker.abundance = 2
end_marker = ClusterMarker("gene4", Motif(3, 3, score=1))
end_marker.promoter = "gene3+gene4"
assert end_marker.abundance == 1
first_cluster = cassis.ClusterPrediction(start_marker, end_marker)
first_cluster.promoters = 3
first_cluster.genes = 4
start_marker = ClusterMarker("gene1", Motif(4, 4, score=1))
start_marker.promoter = "gene1"
assert start_marker.abundance == 1
end_marker = ClusterMarker("gene5", Motif(4, 4, score=1))
end_marker.promoter = "gene5"
assert end_marker.abundance == 1
second_cluster = cassis.ClusterPrediction(start_marker, end_marker)
second_cluster.promoters = 3
second_cluster.genes = 4
# order reversed because subregions are ordered by length when starts are the same
region_predictions = [second_cluster, first_cluster]
record_with_subregions = create_fake_record()
record_without_subregions = create_fake_record(
) # just the same, without adding subregions
subregions = cassis.create_subregions(anchor, region_predictions,
record_with_subregions)
assert record_with_subregions.get_feature_count(
) == record_without_subregions.get_feature_count()
for region in subregions:
record_with_subregions.add_subregion(region)
# test subregion features
expected_count = record_without_subregions.get_feature_count() + len(
subregions)
assert record_with_subregions.get_feature_count() == expected_count
for i, region in enumerate(region_predictions):
subregion = record_with_subregions.get_subregions()[i]
self.assertEqual(subregion.type, "subregion")
self.assertEqual(subregion.tool, "cassis")
self.assertEqual(subregion.anchor, anchor)
self.assertEqual(subregion.get_qualifier("genes"),
(region.genes, ))
self.assertEqual(subregion.get_qualifier("promoters"),
(region.promoters, ))
self.assertEqual(subregion.get_qualifier("gene_left"),
(region.start.gene, ))
self.assertEqual(subregion.get_qualifier("gene_right"),
(region.end.gene, ))
def test_get_promoter_sets(self):
meme_dir = os.path.join(self.options.output_dir, "meme")
anchor_promoter = 5
promoters = [
Promoter("gene1", 1, 1, seq=Seq("acgtacgtacgtacgt")),
Promoter("gene2", 2, 2, seq=Seq("acgtacgtacgtacgt")),
CombinedPromoter("gene3",
"gene4",
3,
4,
seq=Seq("acgtacgtacgtacgt")),
Promoter("gene5", 5, 5, seq=Seq("acgtacgtacgtacgt")),
Promoter("gene6", 6, 6, seq=Seq("acgtacgtacgtacgt")),
# promoter with index=5 --> anchor promoter
Promoter("gene7", 7, 7, seq=Seq("acgtacgtacgtacgt")),
Promoter("gene8", 8, 8, seq=Seq("acgtacgtacgtacgt")),
Promoter("gene9", 9, 9, seq=Seq("acgtacgtacgtacgt"))
]
expected_motifs = [
Motif(plus, minus) for plus in range(3)
for minus in range(3 - plus, 6)
]
self.assertEqual(generate_motifs(meme_dir, anchor_promoter, promoters),
expected_motifs)
def test_get_islands(self):
motifs = [Motif(0, 3, hits={"gene1": 1, "gene2": 2}),
Motif(0, 4, hits={"gene2": 3, "gene4": 2, "gene5": 1})]
# gene2 will be the anchor promoter
anchor_promoter = 1
promoters = []
for i in range(1, 7):
promoters.append(Promoter("gene%d" % i, i * 10, i * 10 + 4))
# resulting in 2 different islands (this example)
# promoter (pos): 1 2 3 4 5 6
# binding sites: 1 2 0 0 0 0
# island: |-|
first_island = Island(promoters[0], promoters[1], motifs[0])
# promoter (pos): 1 2 3 4 5 6
# binding sites: 0 3 0 2 1 0
# island: |---|
second_island = Island(promoters[1], promoters[4], motifs[1])
expected_islands = [first_island, second_island]
assert get_islands(anchor_promoter, motifs, promoters) == expected_islands
def test_sort_by_abundance(self):
islands = []
# island 1: [gene1 -- gene2]
motif = Motif(0, 3, score=3, hits={"gene1": 1, "gene2": 1})
islands.append(Island(Promoter("gene1", 1, 1), Promoter("gene2", 2, 2), motif))
# island 2: [gene2 -- gene5]
motif = Motif(3, 0, score=2, hits={"gene2": 1, "gene3": 1, "gene4": 1, "gene5": 1})
islands.append(Island(Promoter("gene2", 2, 2), Promoter("gene5", 5, 5), motif))
# island 3: [gene1 -- gene5]
motif = Motif(3, 3, score=1, hits={"gene1": 1, "gene2": 1, "gene3": 1, "gene4": 1, "gene5": 1})
islands.append(Island(Promoter("gene1", 1, 1), Promoter("gene5", 5, 5), motif))
# left border: 2x gene1, 1x gene2
# right border: 2x gene5, 1x gene2
expected_clusters = []
# cluster 1: [gene1 -- gene5] --> abundance 2+2 (most abundant)
start = ClusterMarker("gene1", Motif(3, 3, score=1))
start.abundance = 2
end = ClusterMarker("gene5", Motif(3, 3, score=1))
end.abundance = 2
expected_clusters.append(ClusterPrediction(start, end))
# cluster 3: [gene2 -- gene5] --> abundance 1+2, score 2+1 (better/lower)
start = ClusterMarker("gene2", Motif(3, 0, score=2))
start.abundance = 1
end = ClusterMarker("gene5", Motif(3, 3, score=1))
end.abundance = 2
expected_clusters.append(ClusterPrediction(start, end))
# cluster 2: [gene1 -- gene2] --> abundance 2+1, score 1+3 (worse, higher)
start = ClusterMarker("gene1", Motif(3, 3, score=1))
start.abundance = 2
end = ClusterMarker("gene2", Motif(0, 3, score=3))
end.abundance = 1
expected_clusters.append(ClusterPrediction(start, end))
# cluster 4: [gene2 -- gene2] --> abundance 1+1
start = ClusterMarker("gene2", Motif(3, 0, score=2))
start.abundance = 1
end = ClusterMarker("gene2", Motif(0, 3, score=3))
end.abundance = 1
expected_clusters.append(ClusterPrediction(start, end))
# abundance: as high as possible
# score: as low as possible
self.assertEqual(create_predictions(islands), expected_clusters)
