def __init__(self, bridge_sets, ppi_network, transfac_2_network, tag='', pssm_names=None, directory='.'):
self.bridge_sets = bridge_sets
self.ppi_network = ppi_network
self.transfac_2_network = transfac_2_network
self.directory = directory
self.tag = tag
if None == pssm_names:
self.pssm_names = biopsy.get_transfac_pssm_accessions(
biopsy.get_default_transfac_pssm_filter()
)
else:
self.pssm_names = biopsy.SequenceVec()
for name in pssm_names:
self.pssm_names.append(name)
self.sequence_centre_key_regex = re.compile("[Mm]ouse")
self.default_threshold = 0.01
self.override_thresholds = {}
self.default_phylo_threshold = 0.001
self.override_phylo = {}
self.use_max_chain = False
remo_universe = set(chain(*list(names for s, names in bridge_sets.iteritems())))
self.remos = dict(
(
name,
Remo(
name,
self.sequence_centre_key_regex,
self.threshold(name),
self.phylo_threshold(name),
directory = self.directory
)
) for name in remo_universe
)
def __init__(self, factor_synonyms):
self.factor_synonyms = factor_synonyms
for acc in biopsy.get_transfac_pssm_accessions(
biopsy.transfac.PssmFilter.all_pssms()):
for factor in biopsy.transfac.TableLink(acc).entry.factors:
self[acc].add(
self.factor_synonyms.get_synonym(factor.link.entry.name))
def __init__(self,
bridge_sets,
ppi_network,
transfac_2_network,
tag='',
pssm_names=None,
directory='.'):
self.bridge_sets = bridge_sets
self.ppi_network = ppi_network
self.transfac_2_network = transfac_2_network
self.directory = directory
self.tag = tag
if None == pssm_names:
self.pssm_names = biopsy.get_transfac_pssm_accessions(
biopsy.get_default_transfac_pssm_filter())
else:
self.pssm_names = biopsy.SequenceVec()
for name in pssm_names:
self.pssm_names.append(name)
self.sequence_centre_key_regex = re.compile("[Mm]ouse")
self.default_threshold = 0.01
self.override_thresholds = {}
self.default_phylo_threshold = 0.001
self.override_phylo = {}
self.use_max_chain = False
remo_universe = set(
chain(*list(names for s, names in bridge_sets.iteritems())))
self.remos = dict((name,
Remo(name,
self.sequence_centre_key_regex,
self.threshold(name),
self.phylo_threshold(name),
directory=self.directory))
for name in remo_universe)
def test_pssm_distributions():
pssm_acc = 'M00750'
transfac_pssms = biopsy.get_transfac_pssm_accessions(
biopsy.get_default_transfac_pssm_filter())
print 'Got', len(transfac_pssms), 'pssms'
# initialise the distributions
score_dists = {}
for tp in transfac_pssms:
score_dists[tp] = {}
for k in range(1, 100):
score_dists[tp][k] = 0
score_dists['all'] = {}
for k in range(1, 100):
score_dists['all'][k] = 0
# parse the mouse chromosome, score the pssms and fill in the distributions
bases = 0
seq = ''
start = time.clock()
for line in open(
'C:/Data/ensembl/chromosomes/Mus_musculus.NCBIM34.dec.dna.chromosome.1.fa',
'r'):
if line.startswith('>'): continue
seq += line.strip('\r\n').replace('N', '')
# Take 1kb at a time
if len(seq) >= 1000:
hits = biopsy.score_pssms_on_sequence(transfac_pssms, seq, 0.0)
for h in hits:
bin = int(100.0 * h.p_binding)
if 0 != bin:
# print h.binder, bin
score_dists[h.binder][bin] += 1
score_dists['all'][bin] += 1
bases += len(seq)
seq = ''
print 'Bases:', bases
if bases >= 2800000: break
elapsed = time.clock() - start
print 'Scored', len(
transfac_pssms), 'pssms on', bases, 'bases in', elapsed, 'seconds'
print 'Estimate for mouse chromosome 1 (secs):', elapsed * 190000000 / bases
print 'Estimate for mouse chromosome 1 (days):', elapsed * 190000000 / bases / 60 / 60 / 24
print 'Estimate for # bases/hour:', bases * 3600 / elapsed
# remember scores for later
f = open('pssm_p_binding_dists.txt', 'w')
print 'Writing pssm p(binding) distributions to:', f
pickle.dump(score_dists, f)
f.close()
def test_transfac_pssms():
transfac_pssms = biopsy.get_transfac_pssm_accessions( biopsy.get_default_transfac_pssm_filter() )
for p in transfac_pssms:
print p, biopsy.get_transfac_pssm_name( p )
print 'Have', len( transfac_pssms ), 'transfac pssms'
for acc in [ 'R19099', 'M00418' ]:
print acc, biopsy.get_transfac_pssm_name( acc )
biopsy.get_pssm( acc )
print 'Under pssm'
for under_pssm in biopsy.get_pssm( acc ).get_dist( True, False ):
print under_pssm
print 'Under background'
for under_background in biopsy.get_pssm( acc ).get_dist( False, False ):
print under_background
def test_score_fasta():
print '******** test_score_fasta()'
sequences = biopsy.SequenceVec()
for name, seq in biopsy.parse_fasta(
'c:/analysis/keiths/msx1/enhancerD.fa').iteritems():
print name, ':', len(seq), 'bases'
sequences.append(seq)
if len(sequences) >= 3: break
phylo_result = biopsy.score_pssms_on_phylo_sequences(
biopsy.get_transfac_pssm_accessions(
biopsy.get_default_transfac_pssm_filter()), sequences, 0.05)
print 'Max Chain:'
print phylo_result[1]
# print biopsy.sort_hits_by_position( phylo_result[ 0 ] )
print 'Got', len(phylo_result[0]), 'hits from', len(sequences[0]), 'bases'
def test_transfac_pssms():
transfac_pssms = biopsy.get_transfac_pssm_accessions(
biopsy.get_default_transfac_pssm_filter())
for p in transfac_pssms:
print p, biopsy.get_transfac_pssm_name(p)
print 'Have', len(transfac_pssms), 'transfac pssms'
for acc in ['R19099', 'M00418']:
print acc, biopsy.get_transfac_pssm_name(acc)
biopsy.get_pssm(acc)
print 'Under pssm'
for under_pssm in biopsy.get_pssm(acc).get_dist(True, False):
print under_pssm
print 'Under background'
for under_background in biopsy.get_pssm(acc).get_dist(False, False):
print under_background
def test_score_fasta():
print '******** test_score_fasta()'
sequences = biopsy.SequenceVec()
for name, seq in biopsy.parse_fasta( 'c:/analysis/keiths/msx1/enhancerD.fa' ).iteritems():
print name, ':', len( seq ), 'bases'
sequences.append( seq )
if len( sequences) >= 3: break
phylo_result = biopsy.score_pssms_on_phylo_sequences(
biopsy.get_transfac_pssm_accessions( biopsy.get_default_transfac_pssm_filter() ),
sequences,
0.05 )
print 'Max Chain:'
print phylo_result[ 1 ]
# print biopsy.sort_hits_by_position( phylo_result[ 0 ] )
print 'Got', len( phylo_result[ 0 ] ), 'hits from', len( sequences[ 0 ] ), 'bases'
def test_score_pssms():
# 'V$AP1_Q2'
transfac_pssms = biopsy.get_transfac_pssm_accessions(
biopsy.get_default_transfac_pssm_filter())
print 'Got', len(transfac_pssms), 'pssms'
seq = 'tacatcatctgtctgcagtagtctaaccgaccccccccagttttagaagcagactgcatgcggacgggaccgcggatcgcgcggtgcgcctcagtgtacttccgaacgaatgagtcattaatagagcgctatatcgtaactgtctttgacgaagtataccgaaaccgtgcagccagacgtgatccgggcgttgtaaaggcgatcagcgccctaggagtaccatttttgccgtaggcttgcgtctcaaagaccagctggggcgtggtatcactcgtcagtacgatttctgccagatagatagcatagactgaaccttaggcccaatagggacacaattacccgagtgactgactggtctaaggggagtccccccttaaaacgttttacgtaatagcgggctccagaagcaaagcatcggtttgagccccagtactaaacgtttgagtgtttgctctcgtctgataggtaaaccgacaagagaaccaagctcaaggcgcggtaggtgcgccttgcgaactgttgatgccgtgagcgccaccatcccgtgcatcataggcagggagagaagaccacatggccttgcgaccgtatgagctgtttcagattaaatgccaacgggcatggtcggtgtccagcattttttgcagtcagctggtggtacacagtggggacaagaacgcctctggtagatgtcttctgaaggagtaactcatttcgttgaatcgaccttcccttgcgcttgaacgcggacctctagtctctctcgcagactggggtcgaaaatcaaggtagatatggaatgttccgcatgagggtagcgaccggatcgggcgtcaagtatatcctccctgctacgtccccctactagcctcagtccgcctcgaacctaggaagattggccacatcagcttggtggatgcctggtccatacttcagacccgagaatgttagacaggaccccatttggctcctttacgtacgatctatgtagacgcagtga'
# seq = 'acatcat'
# seq = 'gat'
# hits = biopsy.HitVec()
hits = biopsy.score_pssms_on_sequence(transfac_pssms, seq, 0.05)
print hits
print 'score_pssm_on_sequence: Got', len(hits), 'hits from', len(
seq), 'bases'
hits = biopsy.analyse(seq, 0.05)
# print hits
print 'analyse: Got', len(hits), 'hits from', len(seq), 'bases'
def test_remome_analysis():
print '**************** test_remome_analysis ****************'
analysis = biopsy.RemomeAnalysis(
biopsy.Remome.load('C:/Data/ReMos/remo_space.bin'))
analysis.analysis.serialise('analysis.bin')
analysis_copy = biopsy.Analysis.deserialise('analysis.bin')
pssms = biopsy.get_transfac_pssm_accessions(
biopsy.get_default_transfac_pssm_filter())
threshold = 0.05
phylo_threshold = 0.02
try:
analysis.analyse(pssms, threshold, phylo_threshold)
analysis.analysis.serialise('analysis.bin')
except:
analysis.analysis.serialise('analysis.bin')
raise
def test_pssm_distributions():
pssm_acc = 'M00750'
transfac_pssms = biopsy.get_transfac_pssm_accessions( biopsy.get_default_transfac_pssm_filter() )
print 'Got', len( transfac_pssms ), 'pssms'
# initialise the distributions
score_dists = { }
for tp in transfac_pssms:
score_dists[ tp ] = { }
for k in range(1,100):
score_dists[ tp ][ k ] = 0
score_dists[ 'all' ] = { }
for k in range(1,100):
score_dists[ 'all' ][ k ] = 0
# parse the mouse chromosome, score the pssms and fill in the distributions
bases = 0
seq = ''
start = time.clock()
for line in open( 'C:/Data/ensembl/chromosomes/Mus_musculus.NCBIM34.dec.dna.chromosome.1.fa', 'r' ):
if line.startswith( '>' ): continue
seq += line.strip( '\r\n' ).replace( 'N', '' )
# Take 1kb at a time
if len( seq ) >= 1000:
hits = biopsy.score_pssms_on_sequence( transfac_pssms, seq, 0.0 )
for h in hits:
bin = int( 100.0 * h.p_binding )
if 0 != bin:
# print h.binder, bin
score_dists[ h.binder ][ bin ] += 1
score_dists[ 'all' ][ bin ] += 1
bases += len( seq )
seq = ''
print 'Bases:', bases
if bases >= 2800000: break
elapsed = time.clock() - start
print 'Scored', len( transfac_pssms ), 'pssms on', bases, 'bases in', elapsed, 'seconds'
print 'Estimate for mouse chromosome 1 (secs):', elapsed * 190000000 / bases
print 'Estimate for mouse chromosome 1 (days):', elapsed * 190000000 / bases / 60 / 60 / 24
print 'Estimate for # bases/hour:', bases * 3600 / elapsed
# remember scores for later
f = open( 'pssm_p_binding_dists.txt', 'w' )
print 'Writing pssm p(binding) distributions to:', f
pickle.dump(score_dists, f)
f.close()
def test_remome_analysis():
print '**************** test_remome_analysis ****************'
analysis = biopsy.RemomeAnalysis( biopsy.Remome.load('C:/Data/ReMos/remo_space.bin') )
analysis.analysis.serialise( 'analysis.bin' )
analysis_copy = biopsy.Analysis.deserialise( 'analysis.bin' )
pssms = biopsy.get_transfac_pssm_accessions( biopsy.get_default_transfac_pssm_filter() )
threshold = 0.05
phylo_threshold = 0.02
try:
analysis.analyse(
pssms,
threshold,
phylo_threshold
)
analysis.analysis.serialise( 'analysis.bin' )
except:
analysis.analysis.serialise( 'analysis.bin' )
raise
def test_score_pssms():
# 'V$AP1_Q2'
transfac_pssms = biopsy.get_transfac_pssm_accessions( biopsy.get_default_transfac_pssm_filter() )
print 'Got', len( transfac_pssms ), 'pssms'
seq = 'tacatcatctgtctgcagtagtctaaccgaccccccccagttttagaagcagactgcatgcggacgggaccgcggatcgcgcggtgcgcctcagtgtacttccgaacgaatgagtcattaatagagcgctatatcgtaactgtctttgacgaagtataccgaaaccgtgcagccagacgtgatccgggcgttgtaaaggcgatcagcgccctaggagtaccatttttgccgtaggcttgcgtctcaaagaccagctggggcgtggtatcactcgtcagtacgatttctgccagatagatagcatagactgaaccttaggcccaatagggacacaattacccgagtgactgactggtctaaggggagtccccccttaaaacgttttacgtaatagcgggctccagaagcaaagcatcggtttgagccccagtactaaacgtttgagtgtttgctctcgtctgataggtaaaccgacaagagaaccaagctcaaggcgcggtaggtgcgccttgcgaactgttgatgccgtgagcgccaccatcccgtgcatcataggcagggagagaagaccacatggccttgcgaccgtatgagctgtttcagattaaatgccaacgggcatggtcggtgtccagcattttttgcagtcagctggtggtacacagtggggacaagaacgcctctggtagatgtcttctgaaggagtaactcatttcgttgaatcgaccttcccttgcgcttgaacgcggacctctagtctctctcgcagactggggtcgaaaatcaaggtagatatggaatgttccgcatgagggtagcgaccggatcgggcgtcaagtatatcctccctgctacgtccccctactagcctcagtccgcctcgaacctaggaagattggccacatcagcttggtggatgcctggtccatacttcagacccgagaatgttagacaggaccccatttggctcctttacgtacgatctatgtagacgcagtga'
# seq = 'acatcat'
# seq = 'gat'
# hits = biopsy.HitVec()
hits = biopsy.score_pssms_on_sequence(
transfac_pssms,
seq,
0.05 )
print hits
print 'score_pssm_on_sequence: Got', len( hits ), 'hits from', len( seq ), 'bases'
hits = biopsy.analyse(
seq,
0.05)
# print hits
print 'analyse: Got', len( hits ), 'hits from', len( seq ), 'bases'
def pssm_accs():
"The pssms we will test"
pssm_filter = biopsy.transfac.PssmFilter(
# name_regex_pattern = 'NFAT_Q4_01'
)
return biopsy.get_transfac_pssm_accessions(pssm_filter)
print 'Getting database references and names for all matrices'
map = build_map.build_map()
matrix_links = dict((m.acc.as_db_ref(), map.links(m.acc.as_db_ref())) for m in T.Matrix.all())
matrix_names = dict((m.acc.as_db_ref(), names_for_matrix(m)) for m in T.Matrix.all())
# look at one matrix in particular
mat = T.Matrix(1151)
mat_links = map.links(mat.acc.as_db_ref())
myod_gene = T.DbRef.parse_as('17927', T.db.entrez_gene)
bind = psimi.networks['BIND']
myod_in_bind = bind.nodes_for_ref(myod_gene)
myod_node = bind.nodes_for_name('MYOD')[0]
myod_interactor = bind.interactor_for_node(myod_node)
# those pssms we are interested in
pssm_accs = [
T.TableLink(acc)
for acc
in biopsy.get_transfac_pssm_accessions(
biopsy.get_default_transfac_pssm_filter()
)
]
for db_name in psimi.dbs():
print
print db_name
network = psimi.networks[db_name]
print network.summary()
transfac_2_network = psimi.Transfac2Network(map, network)
print transfac_2_network.coverage_summary(pssm_accs)
def __init__(self, factor_synonyms):
self.factor_synonyms = factor_synonyms
for acc in biopsy.get_transfac_pssm_accessions(biopsy.transfac.PssmFilter.all_pssms()):
for factor in biopsy.transfac.TableLink(acc).entry.factors:
self[acc].add(self.factor_synonyms.get_synonym(factor.link.entry.name))
def pssm_accs():
"The pssms we will test"
pssm_filter = biopsy.transfac.PssmFilter(
#name_regex_pattern = 'NFAT_Q4_01'
)
return biopsy.get_transfac_pssm_accessions(pssm_filter)