def test_run(self):
gs = gelscore.Sequence('RGGGNGRGGRGRGGPMGRGGYGGGGSGGGGRGGFPSGGGGGGGQQRAGDWKCPNPTCENMNFSWRNECNQCKAPKPDGPGGGPGGSHMGGN')
dist = gelscore.SimilarityWeight(sim_add=1.0)
rf = gelscore.NormalizedCutRegionFinder(dist, min_region_size=6, score_threshold=0.1)
res = rf.find(gs)
#printRegions(res)
self.assertTrue(len(res) == 3)
def test_run(self):
gs = gelscore.Sequence('GGGGGGGGGGGGGGGGGGGGGGGGGGGGGG')
n = len(gs)
#dist = gelscore.SimilarityDistanceWeight(sim_add=1.0, sd=10.0)
sim = gelscore.SimilarityWeight(sim_add=1.0)
W = sim.weights(gs)
score = sim.score(W)
self.assertAlmostEqual(score, 1.0)
def test_run(self):
gs = gelscore.Sequence('MASNDYTQQATQSYGAYPTQPGQGYSQQSSQPYGQQSYSGYSQSTDTSGYGQSSYSSYGQSQNTGYGTQSTPQGYGSTGGYGSSQSSQSSYGQQSSYPGYGQQPAPSSTSGSYGSSSQSSSYGQPQSGSYSQQPSYGGQQQSYGQQQSYNPPQGYGQQNQYNSSSGGGGGGGGGGNYGQDQSSMSSGGGSGGGYGNQDQSGGGGSGGYGQQDRGGRGRGGSGGGGGGGGGGYNRSSGGYEPRGRGGGRGGRGGMGGSDRGGFNKFGGPRDQGSRHDSEQDNSDNNTIFVQGLGENVTIESVADYFKQIGIIKTNKKTGQPMINLYTDRETGKLKGEATVSFDDPPSAKAAIDWFDGKEFSGNPIKVSFATRRADFNRGGGNGRGGRGRGGPMGRGGYGGGGSGGGGRGGFPSGGGGGGGQQRAGDWKCPNPTCENMNFSWRNECNQCKAPKPDGPGGGPGGSHMGGNYGDDRRGGRGGYDRGGYRGRGGDRGGFRGGRGGGDRGGFGPGKMDSRGEHRQDRRERPY')
#gs = gelscore.Sequence('AAAAAAAAAAAAAYYGSGSGSGSGSAAAAAAAAA')
dist = gelscore.SimilarityWeight(sim_add=1.0)
rf = gelscore.NormalizedCutRegionFinder(dist, min_region_size=10, score_threshold=0.025)
res = rf.find(gs)
#printRegions(res)
self.assertTrue(len(res) >= 7)
def test_run(self):
gs = gelscore.Sequence('AAAAAAAAAAAAGGGGGGGGGGG')
dist = gelscore.SimilarityWeight(sim_add=1.0)
max_regions = 4
comp = gelscore.SequenceCompositionSimilarity()
rf = gelscore.NormalizedCutRegionFinder(dist, min_region_size=6, score_threshold=0.9, max_regions=max_regions)
res = rf.find(gs)
self.assertTrue(len(res) < max_regions)
self.assertTrue(len(res) == 2)
def test_run(self):
# DAD: this is not a passing test right now, doesn't split into 3
gs = gelscore.Sequence('GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGACDEFGHIKLMNPQRSTVWYGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG')
dist = gelscore.SimilarityWeight(sim_add=1.0)
max_regions = 16
rf = gelscore.NormalizedCutRegionFinder(dist, min_region_size=6, score_threshold=0.01, max_regions=max_regions)
res = rf.find(gs)
printRegions(res)
self.assertTrue(len(res) == 3)
def test_run(self):
gs = gelscore.Sequence('GGGGGGGGGGGGGGGGGGGGGGGGGGGGGG')
#dist = gelscore.SimilarityDistanceWeight(sim_add=1.0, sd=10.0)
dist = gelscore.SimilarityWeight(sim_add=1.0)
max_regions = 16
rf = gelscore.NormalizedCutRegionFinder(dist, min_region_size=6, score_threshold=0.3, max_regions=max_regions)
res = rf.find(gs)
#for r in res:
# print r.region.start, r.region.end, r.score, r.norm_score, r.region
self.assertTrue(len(res) == 1)
def test_run(self):
gs = gelscore.Sequence('GGGGGGGGGGAAAAAAAAAAPPPPPPPPPP')
sim = gelscore.SimilarityWeight(sim_add=1.0)
rf = gelscore.NormalizedCutRegionFinder(sim, min_region_size=5, score_threshold=0.8)
D = sim.weights(gs)
#print D
#print D.sum()
#print D[1,:].sum()
#print D[:,1].sum()
res = rf.find(gs)
#print ""
#for r in res:
# print r.score, r.norm_score, r.region
self.assertTrue(len(res) == 3)
# Read input
data = {}
headers = None
if not os.path.isfile(options.in_fname):
raise IOError("# Error: file {} does not exist".format(
options.in_fname))
inf = file(options.in_fname, 'r')
dlr = util.DelimitedLineReader(inf, header=True)
headers = dlr.headers
for flds in dlr.dictentries:
data[flds['id']] = flds
inf.close()
# Distance measure
dist = gelscore.SimilarityWeight(sim_add=1.0, max_distance=200)
# Arrange the data
keys = sorted(data.keys())
for k in keys:
dat = data[k]
seq = dat['sequence']
sim = selfSimilarity(seq, dist)
# Write output
n_written = 0
# Tack sequence on the end, for better readability
columns = headers[:]
columns.remove('sequence')
data_outs.write('\t'.join(columns) + "\tp.value\tp.value.adj\tsequence\n")
orf_gene_dict = dict([(v, k) for (k, v) in gene_orf_dict.items()])
# Select which genes to process
query_keys = []
if not options.query_orf is []:
# Specific ORF(s)
query_keys += options.query_orf
if not options.query_gene is []:
# Specific gene(s)
query_keys += [gene_orf_dict[k] for k in options.query_gene]
if len(query_keys) == 0:
# Go through all proteins in database
query_keys = all_keys
# Distance definition
dist = gelscore.SimilarityWeight(sim_add=1.0,
max_dist=options.max_distance)
# Region finder
reg_finder = gelscore.NormalizedCutRegionFinder(
dist,
min_region_size=options.min_region_size,
score_threshold=options.score_threshold,
max_regions=options.max_regions)
# Remove gaps?
if options.degap:
for k in query_keys:
prot_dict[k] = prot_dict[k].replace("-", '')
if options.debugging:
query_keys = query_keys[0:min(len(query_keys, 100))]
