def score_sequences(sequences, pssms, bg_types, p_binding_sites):
"""
Take a bunch of PSSMs and score sequence sets with them.
"""
scores = cookbook.DictOfLists()
for (dataset, cross_fold_index), pssms_for_fold in pssms.iteritems():
positive_key = positive_sequences_key(dataset, cross_fold_index)
positive_sequence_set = sequences[positive_key]
logging.info('Analysing %d positive sequences (%d bases) for %s-%d',
len(positive_sequence_set),
sum(len(s) for s in positive_sequence_set), dataset,
cross_fold_index)
for p_binding_site in p_binding_sites:
scores[positive_key].append(
score_sequence_set(positive_sequence_set, pssms_for_fold,
p_binding_site))
for bg_type in bg_types:
negative_key = negative_sequences_key(dataset, cross_fold_index,
bg_type)
negative_sequence_set = sequences[negative_key]
logging.info(
'Analysing %d negative sequences (%d bases) for %s-%d-%s',
len(negative_sequence_set),
sum(len(s) for s in negative_sequence_set), dataset,
cross_fold_index, bg_type)
for p_binding_site in p_binding_sites:
scores[negative_key].append(
score_sequence_set(negative_sequence_set, pssms_for_fold,
p_binding_site))
return scores
def get_all_ensembl_go_annotations():
"@return: A map from ensembl genes to sets of go annotations."
import biopsy.identifiers.biomart as biomart
logging.info('Querying Ensembl biomart for all GO annotations')
result = cookbook.DictOfLists()
for id_attr, evidence_attr in [
('go_biological_process_id', 'go_biological_process_linkage_type'),
('go_cellular_component_id', 'go_cellular_component_linkage_type'),
('go_molecular_function_id', 'go_molecular_function_linkage_type'),
]:
for row in biomart.quick_query(
dataset='mmusculus_gene_ensembl',
attributes=['ensembl_gene_id', id_attr, evidence_attr]
):
if row[2] not in options.go_evidence_codes_to_ignore and row[1]:
result[row[0]].append(row[1])
logging.info('Found %d go annotations', sum(len(v) for v in result.values()))
return result
def get_ensembl_go_annotations(genes):
"@return: A map from the given genes to sets of go annotations."
import biopsy.identifiers.biomart as biomart
logging.info('Querying Ensembl biomart for GO annotations of %d genes', len(genes))
result = cookbook.DictOfLists()
for id_attr, evidence_attr in [
('go_biological_process_id', 'go_biological_process_linkage_type'),
('go_cellular_component_id', 'go_cellular_component_linkage_type'),
('go_molecular_function_id', 'go_molecular_function_linkage_type'),
]:
query = biomart.new_query()
dataset = biomart.add_dataset(query, 'mmusculus_gene_ensembl')
biomart.add_attribute(dataset, 'ensembl_gene_id')
biomart.add_attribute(dataset, id_attr)
biomart.add_attribute(dataset, evidence_attr)
filter = biomart.add_filter(dataset, name='ensembl_gene_id', value='')
for chunk in biomart.split_big_list((str(g) for g in genes), 50):
#logging.info('Querying Ensembl biomart for chunk of %d genes', len(chunk))
filter.set('value', ','.join(chunk))
for row in biomart.yield_csv_query_results(query):
if row[2] not in options.go_evidence_codes_to_ignore:
result[row[0]].append(row[1])
logging.info('Found %d go annotations', sum(len(v) for v in result.values()))
return result
f = f_ref.link.entry
if f_ref.species.startswith('mouse') or (f.gene and is_mouse_gene(f.gene.entry)):
for r in refs_for_factor(f):
yield r
def names_for_pssm(m):
for f_ref in m.factors:
f = f_ref.link.entry
yield f.name
for s in f.synonyms:
yield s
if '__main__' == __name__:
import biopsy.transfac as T
matrix_references = cookbook.DictOfLists()
matrix_names = cookbook.DictOfLists()
for m in T.Matrix.all():
acc = m.acc
for r in refs_for_pssm(m):
matrix_references[acc].append(r)
for n in names_for_pssm(m):
matrix_names[acc].append(n)
#for m in T.Site.all():
# refs = [r for r in refs_for_pssm(m)]
# if len(refs):
# references[m] = refs
print 'Found references for %4d of %4d matrices' % (len(matrix_references), len(T.Matrix.all()))