def main():
exon_class_bp.set_debug(0)
exon_type = "CCE"
output = os.path.realpath(os.path.dirname(__file__)).replace(
"src/stretch_calculator", "result/stretch_calculator/")
file_dir = os.path.realpath(os.path.dirname(__file__)).replace(
"src/stretch_calculator", "result/")
if not os.path.isdir(output):
os.mkdir(output)
ctrl_dir = os.path.realpath(
os.path.dirname(__file__)) + "/control_dictionaries/"
sys.path.insert(0, ctrl_dir)
fasterdb = os.path.realpath(os.path.dirname(__file__)).replace(
"src/stretch_calculator", "data/fasterDB_lite.db")
seddb = os.path.realpath(os.path.dirname(__file__)).replace(
"src/stretch_calculator", "data/sed.db")
cnx = sqlite3.connect(fasterdb)
cnx_sed = sqlite3.connect(seddb)
type_factors = ["exon", "spliceosome", "spliceosome"]
regulations = ["down", "down", "up"]
for i in range(len(type_factors)):
type_analysis = type_factors[i]
regulation = regulations[i]
name_file, list_file = initiate_list_of_factor(file_dir, exon_type,
type_analysis)
dict_stretch_3ss = {
"X".join(map(str, stretch_data)):
{nt: []
for nt in config.nt_list}
for stretch_data in config.stretches
}
for j in range(len(name_file)):
if name_file[j] != exon_type:
exon_list = extract_data(cnx, cnx_sed, list_file, name_file, j,
regulation)
for stretch_data in config.stretches:
stretch_dic = get_stretch_score_list(
exon_list, stretch_data)
for nt in config.nt_list:
dict_stretch_3ss["X".join(map(
str, stretch_data))][nt].append(stretch_dic[nt])
else:
for stretch_data in config.stretches:
mod = __import__("%s_stretches" % exon_type)
st_name = "X".join(map(str, stretch_data))
ctrl_dic = eval("mod.stretch_%s" % st_name)
for nt in config.nt_list:
dict_stretch_3ss[st_name][nt].append(ctrl_dic[nt])
for stretch_data in config.stretches:
st_name = "X".join(map(str, stretch_data))
for nt in config.nt_list:
create_figure(
dict_stretch_3ss[st_name][nt], name_file, output,
regulation, "nb_stretch_%s-%s_%s_nt" %
(stretch_data[1], stretch_data[0], nt), type_analysis)
dataframe_creator(
dict_stretch_3ss[st_name][nt], name_file, output,
regulation, "nb_stretch_%s-%s_%s_nt" %
(stretch_data[1], stretch_data[0], nt), type_analysis)
def main_2g(list_file, name_file, exon_type, fasterdb, seddb, output):
"""
:param list_file: (list of str) list of exons files in the form \
of GC_rich_exon file.
:param name_file: (list of str) the name of each files of exons \
given in ``list_file``
:param exon_type: (str) the control exons
:param output: (str) folder where the result will be created
:param seddb: (str) path to sed database
:param fasterdb: (str) path to fasterdb database
"""
list_file.append(None)
name_file.append(exon_type)
exon_class_bp.set_debug(0)
ctrl_dir = os.path.realpath(os.path.dirname(__file__)) + \
"/control_dictionaries/"
sys.path.insert(0, ctrl_dir)
cnx = sqlite3.connect(fasterdb)
cnx_sed = sqlite3.connect(seddb)
type_analysis = "exon"
regulation = "down"
dict_stretch_3ss = {
"X".join(map(str, stretch_data)): {nt: []
for nt in config.nt_list}
for stretch_data in config.stretches
}
for j in range(len(name_file)):
if name_file[j] != exon_type:
exon_list = extract_data(cnx, cnx_sed, list_file, name_file, j,
regulation)
for stretch_data in config.stretches:
stretch_dic = get_stretch_score_list(exon_list, stretch_data)
for nt in config.nt_list:
dict_stretch_3ss["X".join(map(
str, stretch_data))][nt].append(stretch_dic[nt])
else:
for stretch_data in config.stretches:
mod = __import__("%s_stretches" % exon_type)
st_name = "X".join(map(str, stretch_data))
ctrl_dic = eval("mod.stretch_%s" % st_name)
for nt in config.nt_list:
dict_stretch_3ss[st_name][nt].append(ctrl_dic[nt])
for stretch_data in config.stretches:
st_name = "X".join(map(str, stretch_data))
for nt in config.nt_list:
create_figure(
dict_stretch_3ss[st_name][nt], name_file, output, regulation,
"2.2G_nb_stretch_%s-%s_%s_nt" %
(stretch_data[1], stretch_data[0], nt), type_analysis)
dataframe_creator(
dict_stretch_3ss[st_name][nt], name_file, output, regulation,
"2.2G_nb_stretch_%s-%s_%s_nt" %
(stretch_data[1], stretch_data[0], nt), type_analysis)
def main(exon_file, name_table, list_sf, sed, fasterdb, output, ss="5'ss"):
"""
Create a table showing for the exon commons in exon_files files \
their surrounding introns length and their MFE at their 5'ss.
:param exon_file: (str) a file containing gc/at exons
:param name_table: (str) the name of the resulting table
:param list_sf: (List(vtype=str)) list of sf name
:param sed: (str) path to sed database
:param fasterdb: (str) path to fasterdb database
:param output: (str) file were the output will be created
:param ss: (str) the splicing site of interest
"""
sf_names = "_".join([name_table] + list_sf)
exon_class.set_debug(1)
exon_class_bp.set_debug(debug=1)
cnx_sed = sqlite3.connect(sed)
cnx_fasterdb = sqlite3.connect(fasterdb)
exon_list = []
print("Getting exon from file")
exon_list.append(get_exon(exon_file))
print("Getting regulated exons")
for sf in list_sf:
tmp = udf.get_every_events_4_a_sl(cnx_sed, sf, "down")
tmp = [[int(v[0]), int(v[1])] for v in tmp]
exon_list.append(tmp)
print("\t%s : %s down-regulated exons" % (sf, len(tmp)))
new_exon_list = reduce(get_union_exon, exon_list)
print("Commons exons : %s" % len(new_exon_list))
print("Getting commons exons data !")
df = get_exon_data(cnx_sed, new_exon_list, ss)
if ss == "5'ss":
noutput = output + "/rnafold_" + sf_names + "_commons_down_exons/"
print("Computing MFE")
df = computing_mfe(cnx_fasterdb, df, noutput)
else:
# Code to compute number of good branch point
print("Computing Good branch point")
nexon_list = df[["gene_name", "gene_id", "pos"]].values
df2 = svm_bp_finder_launcher(cnx_fasterdb, nexon_list, output)
print(df2.head())
print(df.head())
df = pd.merge(df, df2, how="right", on=["gene_id", "pos"])
print("Writing results !")
df.to_csv("%s/%s_commons_down_exons.csv" % (output, sf_names),
sep="\t",
index=False)
def main():
regulation = "down"
exon_class_bp.set_debug(0)
base = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
output = base + "/result/experimental_branch_point"
at_exon_file = base + "/result/AT_rich_exons"
gc_exon_file = base + "/result/GC_rich_exons"
fasterdb = base + "/data/fasterDB_lite.db"
seddb = base + "/data/sed.db"
cnx = sqlite3.connect(fasterdb)
cnx_sed = sqlite3.connect(seddb)
exon_type = "CCE"
at_exon = read_file(at_exon_file)
gc_exon = read_file(gc_exon_file)
exon2remove = [
list(map(int, exon))
for exon in udf.get_exon_regulated_by_sf(cnx_sed, regulation)
]
ctrl_exons = get_ctrl_exons(cnx, exon_type, exon2remove)
exon_list = gc_exon + at_exon + ctrl_exons
type_exon = ["GC-exons"] * len(gc_exon) + \
["AT-exons"] * len(at_exon) + \
["%s-exons" % exon_type] * len(ctrl_exons)
tot = len(exon_list)
count = 0
count_none = 0
print("Creating bed of predicted branch points")
with open("%s/predicted_branch_points.bed" % output, "w") as outf:
for exon, name_exon in zip(exon_list, type_exon):
exon = exon_class_bp.ExonClass(cnx, str(exon[0]), exon[0], exon[1])
nb_good_bp, list_pos = function_bp.goob_bp_only(exon)
if list_pos is not None:
for line in list_pos:
line[3] += "_" + name_exon
line[0] = "chr" + str(line[0])
outf.write("\t".join(list(map(str, line))) + "\n")
else:
count_none += 1
count += 1
sys.stdout.write("%s/%s (%s) \r" %
(count, tot, count_none))
cnx.close()
cnx_sed.close()
def control_dictionaries_creator():
"""
Create the control dictionary containing the values corresponding to the score of bp and ppt for every control exons
"""
exon_class_bp.set_debug(0)
dir_path = os.path.dirname(os.path.realpath(__file__))
fasterdb = os.path.dirname(os.path.realpath(__file__)).replace(
"src/make_control_files_bp_ppt", "data/fasterDB_lite.db")
seddb = os.path.dirname(os.path.realpath(__file__)).replace(
"src/make_control_files_bp_ppt", "data/sed.db")
ctrl_dir = dir_path + "/control_dictionaries/"
cnx = sqlite3.connect(fasterdb)
cnx_sed = sqlite3.connect(seddb)
exon2remove = union_dataset_function.get_exon_regulated_by_sf(
cnx_sed, "down")
if not os.path.isdir(ctrl_dir):
os.mkdir(ctrl_dir)
exon_type = ["CCE"]
sizes = [100, 50, 35, 25]
for cur_exon_type in exon_type:
ctrl_exon_list = get_control_exon_information(cnx, cur_exon_type,
exon2remove)
print("retrieving upstream intron sequence")
list_exon = [
exon_class_bp.ExonClass(cnx, exon[0], exon[1], exon[2])
for exon in ctrl_exon_list
]
for size in sizes:
print("calculating bp and ppt score")
bp_score_list, ppt_score_list, nb_bp_list, nb_good_bp_list, sequence_list, ag_count_list, \
hbound_list, uaa_list, una_list = function_bp.bp_ppt_calculator(list_exon, size)
cur_file = open(
ctrl_dir + cur_exon_type + "_" + str(size) +
"_bp_ppt_score.py", "w")
cur_file.write("bp_score=" + str(bp_score_list) + "\n")
cur_file.write("ppt_score=" + str(ppt_score_list) + "\n")
cur_file.write("nb_bp=" + str(nb_bp_list) + "\n")
cur_file.write("nb_good_bp=" + str(nb_good_bp_list) + "\n")
cur_file.write("bp_seq=" + str(sequence_list) + "\n")
cur_file.write("ag_count=" + str(ag_count_list) + "\n")
cur_file.write("hbound=" + str(hbound_list) + "\n")
cur_file.write("uaa_count=" + str(uaa_list) + "\n")
cur_file.write("una_count=" + str(una_list) + "\n")
cur_file.close()
"""
Description:
Create a bed file containing every GC and AT exons and their GC frequency
"""
import sqlite3
import os
import sys
mydir = os.path.dirname(os.path.dirname(__file__))
sys.path.insert(0, mydir)
import union_dataset_function as udf
from figure_creator import get_exons_list
bp_dir = mydir + "/make_control_files_bp_ppt"
sys.path.insert(0, bp_dir)
import exon_class_bp
exon_class_bp.set_debug(0)
from function_bp import bp_ppt_calculator
mfe_dir = mydir + "/minimum_free_energy"
sys.path.insert(0, mfe_dir)
import exon_class
exon_class.set_debug(0)
from function import mfe_calculator
stretch_dir = mydir + "/stretch_calculator"
sys.path.insert(0, stretch_dir)
from stretch_calculator import stretch_counter
def get_exon_from_file(exon_file):
"""
Get evey exon stored into ``exon_file``.
def main_2efg(list_file, name_file, exon_type, seddb, fasterdb, output):
"""
:param list_file: (list of str) list of exons files in the form \
of GC_rich_exon file.
:param name_file: (list of str) the name of each files of exons \
given in ``list_file``
:param exon_type: (str) the control exons of interest
:param seddb: (str) path to sed database
:param fasterdb: (str) path to fasterdb database
:param output: (str) path where the result will be created
"""
list_file.append(None)
name_file.append(exon_type)
exon_class_bp.set_debug(0)
ctrl_output = os.path.realpath(os.path.dirname(__file__)).replace(
"src/make_control_files_bp_ppt", "result/make_control_files_bp_ppt/")
if not os.path.isdir(ctrl_output):
os.mkdir(ctrl_output)
ctrl_dir = os.path.realpath(
os.path.dirname(__file__)) + "/control_dictionaries/"
sys.path.insert(0, ctrl_dir)
cnx = sqlite3.connect(fasterdb)
cnx_sed = sqlite3.connect(seddb)
type_analysis = "exon"
regulation = "down"
dict_score_3ss = {
k: {
"bp_score_list": [],
"ppt_score_list": [],
"nb_bp_list": [],
"gc_weblogo": [],
"nb_good_bp_list": [],
"ag_count": [],
"hbound": [],
"uaa_count": [],
"una_count": []
}
for k in [100, 50, 25]
}
for i in range(len(name_file)):
if name_file[i] != exon_type:
exon_list = extract_data(cnx, cnx_sed, list_file, name_file, i,
regulation)
for size in dict_score_3ss.keys():
bp_score_list, ppt_score_list, nb_bp_list, nb_good_bp_list, bp_seq_list, ag_count_list,\
hbound_list, uaa_list, una_list = \
get_bp_ppt_score_list(ctrl_output, exon_list, name_file[i], size, regulation)
dict_score_3ss[size]["nb_good_bp_list"].append(nb_good_bp_list)
if size == 25:
web_logo_creator(
bp_seq_list, "2.1F_%s_%s_exons_%s_nt" %
(name_file[i], regulation, size), output)
dict_score_3ss[size]["gc_weblogo"].append(
get_weblogo_gc_count(bp_seq_list))
dict_score_3ss[size]["hbound"].append(hbound_list)
dict_score_3ss[size]["una_count"].append(una_list)
else:
for size in dict_score_3ss.keys():
mod = __import__("%s_%s_bp_ppt_score" % (exon_type, size))
dict_score_3ss[size]["nb_good_bp_list"].append(mod.nb_good_bp)
dict_score_3ss[size]["gc_weblogo"].append(
get_weblogo_gc_count(mod.bp_seq))
dict_score_3ss[size]["hbound"].append(mod.hbound)
dict_score_3ss[size]["una_count"].append(mod.una_count)
for size in dict_score_3ss.keys():
if size == 25:
create_figure(dict_score_3ss[size]["hbound"], name_file, output,
regulation, "2.2E_nb_h_bound_%s_nt" % size,
type_analysis, "violin")
dataframe_creator(dict_score_3ss[size]["hbound"], name_file,
output, regulation,
"2.2E_nb_h_bound_%s_nt" % size, type_analysis)
create_figure(dict_score_3ss[size]["gc_weblogo"], name_file,
output, regulation, "2.2F_gc_weblogo_%s_nt" % size,
type_analysis, "box")
dataframe_creator(dict_score_3ss[size]["gc_weblogo"], name_file,
output, regulation,
"2.2F_gc_weblogo_%s_nt" % size, type_analysis)
if size == 50:
create_figure(dict_score_3ss[size]["una_count"], name_file, output,
regulation, "2.1G_UNA_count(%snt)" % size,
type_analysis, "box")
dataframe_creator(dict_score_3ss[size]["una_count"], name_file,
output, regulation,
"2.1G_UNA_count(%snt)" % size, type_analysis)
if size == 100:
create_barplot(dict_score_3ss[size]["nb_good_bp_list"], name_file,
output, regulation,
"2.1E_prop_nb_good_branch_point_(%snt)" % size,
type_analysis)
write_proportion_pvalues(
dict_score_3ss[size]["nb_good_bp_list"], name_file, output,
regulation, "2.1E_prop_nb_good_branch_point_(%snt)" % size,
type_analysis)
# dataframe_creator(dict_score_3ss[size]["nb_good_bp_list"], name_file, output, regulation,
# "2.1E_prop_nb_good_branch_point_(%snt)" % size, type_analysis)
cnx.close()
cnx_sed.close()
def main():
exon_class_bp.set_debug(0)
exon_type = "CCE"
ctrl_output = os.path.realpath(os.path.dirname(__file__)).replace(
"src/make_control_files_bp_ppt", "result/make_control_files_bp_ppt/")
output = os.path.realpath(os.path.dirname(__file__)).replace(
"src/make_control_files_bp_ppt", "result/bp_ppt_score/")
file_dir = os.path.realpath(os.path.dirname(__file__)).replace(
"src/make_control_files_bp_ppt", "result/")
if not os.path.isdir(ctrl_output):
os.mkdir(ctrl_output)
if not os.path.isdir(output):
os.mkdir(output)
ctrl_dir = os.path.realpath(
os.path.dirname(__file__)) + "/control_dictionaries/"
sys.path.insert(0, ctrl_dir)
fasterdb = os.path.realpath(os.path.dirname(__file__)).replace(
"src/make_control_files_bp_ppt", "data/fasterDB_lite.db")
seddb = os.path.realpath(os.path.dirname(__file__)).replace(
"src/make_control_files_bp_ppt", "data/sed.db")
cnx = sqlite3.connect(fasterdb)
cnx_sed = sqlite3.connect(seddb)
type_factors = ["exon", "spliceosome", "spliceosome"]
regulations = ["down", "down", "up"]
for j in range(len(type_factors)):
type_analysis = type_factors[j]
regulation = regulations[j]
name_file, list_file = initiate_list_of_factor(file_dir, exon_type,
type_analysis)
dict_score_3ss = {
k: {
"bp_score_list": [],
"ppt_score_list": [],
"nb_bp_list": [],
"gc_weblogo": [],
"nb_good_bp_list": [],
"ag_count": [],
"hbound": [],
"uaa_count": [],
"una_count": []
}
for k in [100, 50, 35, 25]
}
# list_force_acceptor = []
# list_force_donor = []
for i in range(len(name_file)):
if name_file[i] != exon_type:
exon_list = extract_data(cnx, cnx_sed, list_file, name_file, i,
regulation)
# list_force_acceptor.append(get_redundant_list_of_value(cnx_sed, exon_list, "force_acceptor"))
# list_force_donor.append(get_redundant_list_of_value(cnx_sed, exon_list, "force_donor"))
for size in dict_score_3ss.keys():
bp_score_list, ppt_score_list, nb_bp_list, nb_good_bp_list, bp_seq_list, ag_count_list,\
hbound_list, uaa_list, una_list = \
get_bp_ppt_score_list(ctrl_output, exon_list, name_file[i], size, regulation)
# dict_score_3ss[size]["bp_score_list"].append(bp_score_list)
# dict_score_3ss[size]["ppt_score_list"].append(ppt_score_list)
# dict_score_3ss[size]["nb_bp_list"].append(nb_bp_list)
dict_score_3ss[size]["nb_good_bp_list"].append(
nb_good_bp_list)
web_logo_creator(
bp_seq_list,
"%s_%s_exons_%s_nt" % (name_file[i], regulation, size),
output)
dict_score_3ss[size]["gc_weblogo"].append(
get_weblogo_gc_count(bp_seq_list))
# dict_score_3ss[size]["ag_count"].append(ag_count_list)
dict_score_3ss[size]["hbound"].append(hbound_list)
# dict_score_3ss[size]["uaa_count"].append(uaa_list)
dict_score_3ss[size]["una_count"].append(una_list)
else:
for size in dict_score_3ss.keys():
mod = __import__("%s_%s_bp_ppt_score" % (exon_type, size))
# dict_score_3ss[size]["bp_score_list"].append(mod.bp_score)
# dict_score_3ss[size]["ppt_score_list"].append(mod.ppt_score)
# dict_score_3ss[size]["nb_bp_list"].append(mod.nb_bp)
dict_score_3ss[size]["nb_good_bp_list"].append(
mod.nb_good_bp)
web_logo_creator(
mod.bp_seq,
"%s_%s_exons_%s_nt" % (name_file[i], regulation, size),
output)
dict_score_3ss[size]["gc_weblogo"].append(
get_weblogo_gc_count(mod.bp_seq))
# dict_score_3ss[size]["ag_count"].append(mod.ag_count)
dict_score_3ss[size]["hbound"].append(mod.hbound)
# dict_score_3ss[size]["uaa_count"].append(mod.uaa_count)
dict_score_3ss[size]["una_count"].append(mod.una_count)
# list_force_acceptor.append(get_control_exon_information(cnx_sed, exon_type, "force_acceptor"))
# list_force_donor.append(get_control_exon_information(cnx_sed, exon_type, "force_donor"))
for size in dict_score_3ss.keys():
print("------------> %s nt " % size)
create_barplot(dict_score_3ss[size]["nb_good_bp_list"], name_file,
output, regulation,
"prop_nb_good_branch_point_(%snt)" % size,
type_analysis)
# create_distplot_bp(dict_score_3ss[size]["nb_good_bp_list"], name_file, output, regulation,
# "prop_nb_good_branch_point_(%snt)" % size, type_analysis)
# write_proportion_pvalues(dict_score_3ss[size]["nb_good_bp_list"], name_file, output, regulation,
# "prop_nb_good_branch_point_(%snt)" % size, type_analysis)
dataframe_creator(dict_score_3ss[size]["nb_good_bp_list"],
name_file, output, regulation,
"prop_nb_good_branch_point_(%snt)" % size,
type_analysis)
# create_barplot(dict_score_3ss[size]["ag_count"], name_file, output, regulation,
# "AG_count_downstream_bp(%snt)" % size, type_analysis)
# write_proportion_pvalues(dict_score_3ss[size]["ag_count"], name_file, output, regulation,
# "AG_count_downstream_bp(%snt)" % size, type_analysis)
# create_barplot(dict_score_3ss[size]["uaa_count"], name_file, output, regulation,
# "UAA_count(%snt)" % size, type_analysis)
# write_proportion_pvalues(dict_score_3ss[size]["uaa_count"], name_file, output, regulation,
# "UAA_count(%snt)" % size, type_analysis)
create_figure(dict_score_3ss[size]["una_count"], name_file, output,
regulation, "UNA_count(%snt)" % size, type_analysis,
"box")
dataframe_creator(dict_score_3ss[size]["una_count"], name_file,
output, regulation, "UNA_count(%snt)" % size,
type_analysis)
# write_proportion_pvalues(dict_score_3ss[size]["una_count"], name_file, output, regulation,
# "UNA_count(%snt)" % size, type_analysis)
create_figure(dict_score_3ss[size]["hbound"], name_file, output,
regulation, "nb_h_bound_%s_nt" % size, type_analysis,
"violin")
dataframe_creator(dict_score_3ss[size]["hbound"], name_file,
output, regulation, "nb_h_bound_%s_nt" % size,
type_analysis)
create_figure(dict_score_3ss[size]["gc_weblogo"], name_file,
output, regulation, "gc_weblogo_%s_nt" % size,
type_analysis, "box")
dataframe_creator(dict_score_3ss[size]["gc_weblogo"], name_file,
output, regulation, "gc_weblogo_%s_nt" % size,
type_analysis)