def test_record_basic(self):
"""Basic test on Record
"""
def pbool(b):
if b:
return 1
return 0
r = Fasta.Record()
assert pbool(type(r.title) is StringType) # StringType
assert pbool(type(r.sequence) is StringType) # StringType
def __str__(self):
"""Print out a fasta version of the alignment info."""
return_string = ''
for item in self._records:
new_f_record = Fasta.Record()
new_f_record.title = item.description
new_f_record.sequence = item.seq.data
return_string = return_string + str(new_f_record) + os.linesep + os.linesep
# have a extra newline, so strip two off and add one before returning
return return_string.rstrip() + os.linesep
def main(blast_file):
db_dir = os.path.join(os.getcwd(), "db")
cur_dbs = get_available_dbs(db_dir)
length_cutoff = 0.2
blast_clusters, all_lengths = get_blast_clusters(blast_file, length_cutoff)
filter_clusters = filter_by_organism(blast_clusters, org_includes, cur_dbs)
length_plot(all_lengths, blast_file)
cluster_grouper = SimilarityClusterGrouper(2, 200, [(0.9, 10)])
all_groups = cluster_grouper.get_final_groups(filter_clusters)
base, ext = os.path.splitext(blast_file)
cluster_file = base + "-bcluster%s.txt"
for gindex, group in enumerate(all_groups):
print '-----------'
with open(cluster_file % gindex, "w") as out_handle:
for gitem in group:
db_rec = get_db_rec(gitem, cur_dbs)
print gitem, db_rec["org_scientific_name"]
rec = Fasta.Record()
rec.title = gitem
rec.sequence = db_rec["seq"]
out_handle.write(str(rec) + "\n")
def main(ipr_number, num_clusters, out_dir):
charge_window = 75
db_dir = os.path.join(os.getcwd(), "db")
cur_db = shelve.open(os.path.join(db_dir, ipr_number))
tax_graph = build_tax_graph(cur_db)
uniprot_ids = []
info_array = []
for db_domain in cur_db.keys():
if not cur_db[db_domain].get("is_uniref_child", ""):
uniprot_ids.append(db_domain)
db_item = cur_db[db_domain]
cur_cluster_info = [
float(db_item["charge"]),
float(db_item["charge_region"]) * 10.0,
len(db_item.get("db_refs", [])) * 5.0,
calc_domain_distance(db_item) * 50.0,
#max(len(db_item.get("string_interactors", [])) - 1, 0),
]
info_array.append(cur_cluster_info)
info_array = numpy.array(info_array)
print 'Num genes', len(info_array), num_clusters
cluster_ids, error, nfound = Cluster.kcluster(
info_array, nclusters=num_clusters, npass=50) #, method='a', dist='c')
#tree = Cluster.treecluster(info_array, method='a', dist='c')
#cluster_ids = tree.cut(num_clusters)
cluster_dict = collections.defaultdict(lambda: [])
for i, cluster_id in enumerate(cluster_ids):
cluster_dict[cluster_id].append(uniprot_ids[i])
out_seq_file = os.path.join(out_dir, "%s-seqs.fa" % (ipr_number))
out_seq_handle = open(out_seq_file, "w")
for index, cluster_group in enumerate(cluster_dict.values()):
print '***********', index
org_dists = []
for uniprot_id in cluster_group:
org = cur_db[uniprot_id]["org_scientific_name"]
distance = networkx.dijkstra_path_length(tax_graph, 'Mus musculus',
org)
org_dists.append((distance, org, uniprot_id))
org_dists.sort()
members = []
for d, o, u in org_dists:
charge_plot_img = calc_charge_plot(u, cur_db[u], charge_window,
out_dir)
base, ext = os.path.splitext(charge_plot_img)
disorder_plot_img = "%s-idr%s" % (base, ext)
rec = Fasta.Record()
rec.title = u
rec.sequence = cur_db[u]["seq"]
out_seq_handle.write(str(rec) + "\n")
members.append(
dict(
organism=o,
uniprot_id=get_uniprot_links([u]),
alt_names=get_alt_names(cur_db[u]),
alt_ids=get_uniprot_links(cur_db[u].get(
"uniref_children", [])),
charge=cur_db[u]["charge"],
charge_region="%0.2f" % cur_db[u]["charge_region"],
charge_plot_img=charge_plot_img,
disorder_plot_img=disorder_plot_img,
domains=len(cur_db[u].get("db_refs", [])),
interactions=get_string_link(
u,
max(
len(cur_db[u].get("string_interactors", [])) - 1,
0)),
description=cur_db[u].get("function_descr", " "),
c_distance="%0.2f" % calc_domain_distance(cur_db[u]),
))
with open(
os.path.join(out_dir,
"%s-cluster%s.html" % (ipr_number, index)),
"w") as out_handle:
tmpl = Template(cluster_template)
out_handle.write(tmpl.render(cluster_members=members))
