def extract_zhang(file_name, type):
names_to_types = {}
loader = TableLoader()
print file_name
print "*" * 100
entries = loader.load("zhang/final/%s" % file_name)
for entry in entries:
# print entry
# i+=1
#
# if i == 10:
# break
name = entry["name"]
if "/" in name:
print name, "might not be a srna type"
if not names_to_types.has_key(name):
names_to_types[name] = type
else:
print "[warning] multiple entries of the same name"
for key, value in names_to_types.items():
print "name: %s\t|\t" % key, "type: %s" % value
def extract_zhang(file_name, type):
names_to_types = {}
loader = TableLoader()
print file_name
print "*" * 100
entries = loader.load("zhang/final/%s" % file_name)
for entry in entries:
# print entry
# i+=1
#
# if i == 10:
# break
name = entry["name"]
if "/" in name:
print name, "might not be a srna type"
if not names_to_types.has_key(name):
names_to_types[name] = type
else:
print "[warning] multiple entries of the same name"
for key, value in names_to_types.items():
print "name: %s\t|\t" % key, "type: %s" % value
def extract_bilusic(file_name, type):
names_to_types = {}
loader = TableLoader()
print file_name
print "*" * 100
entries = loader.load("bilusic/final/%s" % file_name)
for entry in entries:
# print entry
# i+=1
#
# if i == 10:
# break
name = entry["name"]
if not names_to_types.has_key(name):
names_to_types[name] = type
if "5'utr" in name:
names_to_types[name] = "5'utr"
elif "3'utr" in name:
names_to_types[name] = "3'utr"
else:
print "[warning] multiple entries of the same name"
for key, value in names_to_types.items():
print "name: %s\t|\t" % key, "type: %s" % value
def extract_bilusic(file_name, type):
names_to_types = {}
loader = TableLoader()
print file_name
print "*" * 100
entries = loader.load("bilusic/final/%s" % file_name)
for entry in entries:
# print entry
# i+=1
#
# if i == 10:
# break
name = entry["name"]
if not names_to_types.has_key(name):
names_to_types[name] = type
if "5'utr" in name:
names_to_types[name] = "5'utr"
elif "3'utr" in name:
names_to_types[name] = "3'utr"
else:
print "[warning] multiple entries of the same name"
for key, value in names_to_types.items():
print "name: %s\t|\t" % key, "type: %s" % value
def extract_our_gens():
files = ["assign-type-to-all-chimeras-of-Iron_limitation_CL_FLAG207_208_305_all_fragments_l25.txt_all_interactions.with-type",
"assign-type-to-all-chimeras-of-Log_phase_CL_FLAG101-104_108_109_all_fragments_l25.txt_all_interactions.with-type",
"assign-type-to-all-chimeras-of-MG_hfq-WT101_cutadapt_bwa.bam_all_fragments_l25.txt_all_interactions.with-type",
"assign-type-to-all-chimeras-of-MG_hfq-wt202_CL_Stationary_cutadapt_bwa.bam_all_fragments_l25.txt_all_interactions.with-type",
"assign-type-to-all-chimeras-of-Stationary_CL_FLAG209_210_312_all_fragments_l25.txt_all_interactions.with-type",
"assign-type-to-signif-chimeras-of-Iron_limitation_CL_FLAG207_208_305_all_fragments_l25.txt_sig_interactions.with-type",
"assign-type-to-signif-chimeras-of-Log_phase_CL_FLAG101-104_108_109_all_fragments_l25.txt_sig_interactions.with-type",
"assign-type-to-signif-chimeras-of-Stationary_CL_FLAG209_210_312_all_fragments_l25.txt_sig_interactions.with-type",
"assign-type-to-single-counts-of-Iron_limitation_CL_FLAG207_208_305_all_fragments_l25.txt_single_counts.with-type",
"assign-type-to-single-counts-of-Log_phase_CL_FLAG101-104_108_109_all_fragments_l25.txt_single_counts.with-type",
"assign-type-to-single-counts-of-MG_hfq-WT101_cutadapt_bwa.bam_all_fragments_l25.txt_single_counts.with-type",
"assign-type-to-single-counts-of-MG_hfq-wt202_CL_Stationary_cutadapt_bwa.bam_all_fragments_l25.txt_single_counts.with-type",
"assign-type-to-single-counts-of-Stationary_CL_FLAG209_210_312_all_fragments_l25.txt_single_counts.with-type"]
names_to_types = {}
loader = TableLoader()
for file_name in files:
i = 0
print file_name
print "*" * 100
entries = loader.load("our_files/%s" % file_name)
for entry in entries:
# print entry
# i+=1
#
# if i == 10:
# break
name = entry["rna1 name"]
if not names_to_types.has_key(name):
names_to_types[name] = entry["first_type"]
name = entry["rna2 name"]
if not names_to_types.has_key(name):
names_to_types[name] = entry["second_type"]
for key, value in names_to_types.items():
print "name: %s\t|\t" % key, "type: %s" % value
def extract_our_gens():
files = [
"assign-type-to-all-chimeras-of-Iron_limitation_CL_FLAG207_208_305_all_fragments_l25.txt_all_interactions.with-type",
"assign-type-to-all-chimeras-of-Log_phase_CL_FLAG101-104_108_109_all_fragments_l25.txt_all_interactions.with-type",
"assign-type-to-all-chimeras-of-MG_hfq-WT101_cutadapt_bwa.bam_all_fragments_l25.txt_all_interactions.with-type",
"assign-type-to-all-chimeras-of-MG_hfq-wt202_CL_Stationary_cutadapt_bwa.bam_all_fragments_l25.txt_all_interactions.with-type",
"assign-type-to-all-chimeras-of-Stationary_CL_FLAG209_210_312_all_fragments_l25.txt_all_interactions.with-type",
"assign-type-to-signif-chimeras-of-Iron_limitation_CL_FLAG207_208_305_all_fragments_l25.txt_sig_interactions.with-type",
"assign-type-to-signif-chimeras-of-Log_phase_CL_FLAG101-104_108_109_all_fragments_l25.txt_sig_interactions.with-type",
"assign-type-to-signif-chimeras-of-Stationary_CL_FLAG209_210_312_all_fragments_l25.txt_sig_interactions.with-type",
"assign-type-to-single-counts-of-Iron_limitation_CL_FLAG207_208_305_all_fragments_l25.txt_single_counts.with-type",
"assign-type-to-single-counts-of-Log_phase_CL_FLAG101-104_108_109_all_fragments_l25.txt_single_counts.with-type",
"assign-type-to-single-counts-of-MG_hfq-WT101_cutadapt_bwa.bam_all_fragments_l25.txt_single_counts.with-type",
"assign-type-to-single-counts-of-MG_hfq-wt202_CL_Stationary_cutadapt_bwa.bam_all_fragments_l25.txt_single_counts.with-type",
"assign-type-to-single-counts-of-Stationary_CL_FLAG209_210_312_all_fragments_l25.txt_single_counts.with-type"
]
names_to_types = {}
loader = TableLoader()
for file_name in files:
i = 0
print file_name
print "*" * 100
entries = loader.load("our_files/%s" % file_name)
for entry in entries:
# print entry
# i+=1
#
# if i == 10:
# break
name = entry["rna1 name"]
if not names_to_types.has_key(name):
names_to_types[name] = entry["first_type"]
name = entry["rna2 name"]
if not names_to_types.has_key(name):
names_to_types[name] = entry["second_type"]
for key, value in names_to_types.items():
print "name: %s\t|\t" % key, "type: %s" % value
def __init__(self, our_tables_list, article_tables_list):
loader = OurTableLoader()
self._our_tables_list = \
[loader.createTable(name, loader.loadUnprocessed(path)) for name, path in our_tables_list]
loader = TableLoader()
self._article_tables_list = [loader.createTable(name, loader.load(path)) for name, path in article_tables_list]
def generate_table_old(table_path, name, is_our_table=False):
if not is_our_table:
loader = TableLoader()
table = loader.createTable(name, loader.load(table_path))
else:
loader = OurTableLoader()
table = loader.createTable(name, loader.loadUnprocessed(table_path))
db = MySQLdb.connect(host="localhost",
user="******",
db="article_refactor_24_3_2016")
cur = db.cursor(MySQLdb.cursors.DictCursor)
# Generate the keys for the table
id_keys = [
TableGlobals.FIRST_START_BASE_KEY, TableGlobals.FIRST_END_BASE_KEY,
TableGlobals.FIRST_STRAND_KEY, TableGlobals.SECOND_START_BASE_KEY,
TableGlobals.SECOND_END_BASE_KEY, TableGlobals.SECOND_STRAND_KEY
]
for key in table._dctData.values()[0].keys():
if key not in id_keys:
id_keys.append(key)
fields = ", ".join("%s VARCHAR(200)" % key.replace(" ", "_").replace(
"-", "_").replace("'", "").replace("/", "") for key in id_keys
if key != "")
print fields
cur.execute("CREATE TABLE %s (%s)" % (table.get_name(), fields))
table_as_list = []
# Generate dictionary for each row according to the keys
for key, value in table:
id_values = key.split(Table.ID_DELIMITER)
for id_key, id_val in zip(id_keys, id_values):
value[id_key] = str(id_val)
table_as_list.append(value)
# print "-->", value
# Go over the rows and add them to the db
for row in table_as_list:
values = ",".join("%s" % db.literal(str(row[key])) for key in id_keys
if key != "")
# print "INSERT INTO %s VALUES (%s)" % (table.get_name(), values)
cur.execute("INSERT INTO %s VALUES (%s)" % (table.get_name(), values))
db.commit()
def generate_table_old(table_path, name, is_our_table=False):
if not is_our_table:
loader = TableLoader()
table = loader.createTable(name, loader.load(table_path))
else:
loader = OurTableLoader()
table = loader.createTable(name, loader.loadUnprocessed(table_path))
db=MySQLdb.connect(host="localhost",user="******",db="article_refactor_24_3_2016")
cur = db.cursor(MySQLdb.cursors.DictCursor)
# Generate the keys for the table
id_keys = [TableGlobals.FIRST_START_BASE_KEY,
TableGlobals.FIRST_END_BASE_KEY,
TableGlobals.FIRST_STRAND_KEY,
TableGlobals.SECOND_START_BASE_KEY,
TableGlobals.SECOND_END_BASE_KEY,
TableGlobals.SECOND_STRAND_KEY]
for key in table._dctData.values()[0].keys():
if key not in id_keys:
id_keys.append(key)
fields = ", ".join("%s VARCHAR(200)" % key.replace(" ", "_").replace("-", "_").replace("'", "").replace("/", "") for key in id_keys if key != "")
print fields
cur.execute("CREATE TABLE %s (%s)" % (table.get_name(), fields))
table_as_list = []
# Generate dictionary for each row according to the keys
for key, value in table:
id_values = key.split(Table.ID_DELIMITER)
for id_key, id_val in zip(id_keys, id_values):
value[id_key] = str(id_val)
table_as_list.append(value)
# print "-->", value
# Go over the rows and add them to the db
for row in table_as_list:
values = ",".join("%s" % db.literal(str(row[key])) for key in id_keys if key != "")
# print "INSERT INTO %s VALUES (%s)" % (table.get_name(), values)
cur.execute("INSERT INTO %s VALUES (%s)" % (table.get_name(), values))
db.commit()
def lybecker_update(file_name,
show_warnings=True,
overlap_delimiter="/",
overlap_field="annotation of overlapping genes",
adjacent_field="adjacent genes",
loader_type=LybeckerS2TableLoader):
geneLoader = GeneTableLoader()
gene_table = geneLoader.createTable("genes", geneLoader.loadUnprocessed("./genes.col"))
loader = loader_type()
table = loader.createTable("lybecker", loader.load("lybecker/final/%s" % file_name))
new_table_raw = []
for id, info in table:
dct = {}
info.pop(Table.UNIQUE_ID_FIELD)
start, end, strand = id.split(";")[:3]
# print info["name"]
start = int(start)
end = int(end)
result = gene_table.is_overlaps(start, end, "none")
if show_warnings:
print id
overlaps = [gene for gene in info[overlap_field].split(overlap_delimiter) if gene != ""]
print overlaps
for index, gene in enumerate(result):
print "%d: %s" % (index, gene[1][1]["name"])
if gene[1][1]["name"] not in overlaps:
print "[warning] older name is being used"
if len(overlaps) > len(result):
print "[warning] missing overlapping gene"
if len(overlaps) < len(result):
print "[warning] extra overlapping gene"
# Set the record location
dct[TableGlobals.FIRST_START_BASE_KEY], dct[TableGlobals.FIRST_END_BASE_KEY], \
dct[TableGlobals.FIRST_STRAND_KEY], dct[TableGlobals.SECOND_START_BASE_KEY], \
dct[TableGlobals.SECOND_END_BASE_KEY], dct[TableGlobals.SECOND_STRAND_KEY] = id.split(Table.ID_DELIMITER)
# Assume unknown strand
dct[TableGlobals.FIRST_STRAND_KEY] = "none"
dct[TableGlobals.SECOND_STRAND_KEY] = "none"
is_valid = result[0][0]
overlap_names = info[overlap_field].split(overlap_delimiter)
# Check if major in overlapping names
if not is_valid:
is_valid = False
# print "major names", [gene[1][1]["name"] for gene in result]
for first in [gene[1][1]["name"] for gene in result]:
for second in overlap_names:
if first in second:
is_valid = True
break
if is_valid:
break
# check if minor in overlapping names
if not is_valid:
is_valid = False
other_names = []
for gene in result:
other_names.extend(gene[1][1]["other_names"])
# print "old names", other_names
for first in other_names:
for second in overlap_names:
if first in second:
is_valid = True
break
if is_valid:
break
if "intergenic" == info["category"]:
is_valid = False
adjacent_genes = [val for val in info[adjacent_field].split(overlap_delimiter) if val != ""]
first = gene_table.findByName(adjacent_genes[0])
second = gene_table.findByName(adjacent_genes[1])
if first == (None, None):
first = gene_table.findByOtherNames(adjacent_genes[0])
if second == (None, None):
second = gene_table.findByOtherNames(adjacent_genes[1])
if first != (None, None):
representing = first
elif second != (None, None):
representing = second
else:
raise BaseException("No presenting gene found")
strand = representing[0].split(Table.ID_DELIMITER)[2]
if strand == TableGlobals.STRAND_POSITIVE:
dct[TableGlobals.FIRST_STRAND_KEY] = TableGlobals.STRAND_NEGATIVE
dct[TableGlobals.SECOND_STRAND_KEY] = TableGlobals.STRAND_NEGATIVE
else:
dct[TableGlobals.FIRST_STRAND_KEY] = TableGlobals.STRAND_POSITIVE
dct[TableGlobals.SECOND_STRAND_KEY] = TableGlobals.STRAND_POSITIVE
if is_valid:
is_valid = "divergent" not in info["category"] and \
"convergent" not in info["category"]
# Update the record name if gene was found
if is_valid:
info["name"] = "overlapping_"
else:
name_id = id.split(Table.ID_DELIMITER)[:2]
name_id.append(dct[TableGlobals.FIRST_STRAND_KEY])
info["name"] = "lybecker_%s_%s" % (info["category"], Table.ID_DELIMITER.join(name_id))
pos_count = 0
neg_count = 0
if is_valid:
# for each gene match
for entry in result:
# exact match add name
info["name"] += "%s." % entry[1][1]["name"]
strand = entry[1][0].split(Table.ID_DELIMITER)[2]
if TableGlobals.STRAND_NEGATIVE == strand:
neg_count += 1
if TableGlobals.STRAND_POSITIVE == strand:
pos_count += 1
if neg_count == 0:
dct[TableGlobals.FIRST_STRAND_KEY] = TableGlobals.STRAND_NEGATIVE
dct[TableGlobals.SECOND_STRAND_KEY] = TableGlobals.STRAND_NEGATIVE
elif pos_count == 0:
dct[TableGlobals.FIRST_STRAND_KEY] = TableGlobals.STRAND_POSITIVE
dct[TableGlobals.SECOND_STRAND_KEY] = TableGlobals.STRAND_POSITIVE
else:
print "no strand match: %s" % entry[1][1]["name"]
# remove extra . from name if match found
if is_valid:
info["name"] = info["name"][:-1]
dct.update(info)
new_table_raw.append(dct)
if show_warnings:
print 20 * "*"
# for row in new_table_raw:
# print row
TableLoader().createTable("updated_lybecker", new_table_raw).dump("lybecker/final/updated_%s" % file_name)
def generate_zhang_stats():
loader = TableLoader()
rows_as_dictionary = loader.load("output/table_s6_range_0.csv")
row_list = []
header = ["name",
"il_rna2_percent",
"stat_rna2_percent",
"log_rna2_percent",
"k31_ip", # distal
"r16a_ip", # rim
"q8a_ip", # proximal
"k31",
"r16a",
"q8a",
"average_percent"]
for row in rows_as_dictionary:
new_row = [row["name"],
row["signif_chimeras_of_iron_limitation_cl.as_rna2_percentage"].replace("-", ""),
row["signif_chimeras_of_stationary_cl.as_rna2_percentage"].replace("-", ""),
row["signif_chimeras_of_log_phase_cl.as_rna2_percentage"].replace("-", "")]
matches = get_zhang_stats_by_name(row["name"])
if len(matches) > 1:
print "warning too many results"
elif len(matches) == 1:
new_row.extend(val for val in matches[0])
# print new_row
else:
new_row.extend([""] * 6)
average_percent = 0.0
fields = 0
if row["signif_chimeras_of_iron_limitation_cl.as_rna2_percentage"] != "-":
average_percent += float(row["signif_chimeras_of_iron_limitation_cl.as_rna2_percentage"])
fields += 1
if row["signif_chimeras_of_stationary_cl.as_rna2_percentage"] != "-":
average_percent += float(row["signif_chimeras_of_stationary_cl.as_rna2_percentage"])
fields += 1
if row["signif_chimeras_of_log_phase_cl.as_rna2_percentage"] != "-":
average_percent += float(row["signif_chimeras_of_log_phase_cl.as_rna2_percentage"])
fields += 1
average_percent /= fields
new_row.append(average_percent)
row_list.append(new_row)
# for row in row_list:
# print row
fl = open("zhang_stats.csv", "wb")
fl.write("%s\n" % "\t".join(header))
for row in row_list:
fl.write("%s\n" % "\t".join(str(val) for val in row))
fl.close()
def generate_zhang_stats():
loader = TableLoader()
rows_as_dictionary = loader.load("output/table_s6_range_0.csv")
row_list = []
header = [
"name",
"il_rna2_percent",
"stat_rna2_percent",
"log_rna2_percent",
"k31_ip", # distal
"r16a_ip", # rim
"q8a_ip", # proximal
"k31",
"r16a",
"q8a",
"average_percent"
]
for row in rows_as_dictionary:
new_row = [
row["name"],
row["signif_chimeras_of_iron_limitation_cl.as_rna2_percentage"].
replace("-", ""),
row["signif_chimeras_of_stationary_cl.as_rna2_percentage"].replace(
"-", ""),
row["signif_chimeras_of_log_phase_cl.as_rna2_percentage"].replace(
"-", "")
]
matches = get_zhang_stats_by_name(row["name"])
if len(matches) > 1:
print "warning too many results"
elif len(matches) == 1:
new_row.extend(val for val in matches[0])
# print new_row
else:
new_row.extend([""] * 6)
average_percent = 0.0
fields = 0
if row["signif_chimeras_of_iron_limitation_cl.as_rna2_percentage"] != "-":
average_percent += float(
row["signif_chimeras_of_iron_limitation_cl.as_rna2_percentage"]
)
fields += 1
if row["signif_chimeras_of_stationary_cl.as_rna2_percentage"] != "-":
average_percent += float(
row["signif_chimeras_of_stationary_cl.as_rna2_percentage"])
fields += 1
if row["signif_chimeras_of_log_phase_cl.as_rna2_percentage"] != "-":
average_percent += float(
row["signif_chimeras_of_log_phase_cl.as_rna2_percentage"])
fields += 1
average_percent /= fields
new_row.append(average_percent)
row_list.append(new_row)
# for row in row_list:
# print row
fl = open("zhang_stats.csv", "wb")
fl.write("%s\n" % "\t".join(header))
for row in row_list:
fl.write("%s\n" % "\t".join(str(val) for val in row))
fl.close()
def format_final_table(path, our_tables, output_file):
sets = {
"Raghavan et al 2011":
["raghavan_s5", "raghavan_s6", "raghavan_s7", "raghavan_2"],
"Lybecker et al 2014": ["lybecker_s1", "lybecker_s2"],
"Bilusic et al 2014": [
"bilusic_s1", "bilusic_s2", "bilusic_s3_1", "bilusic_s3_2",
"bilusic_s4_1", "bilusic_s4_2"
]
# "zhang": ["zhang_s3_2013_sheet2008", "zhang_s3_2013_sheet2009", "zhang_s4_2013_sheet2008", "zhang_s4_2013_sheet2009"],
}
mcdowell_set = {"McDowall et al 2014": ["mcdowell"]}
thomason_set = {
"Thomason et al 2015": [
"thomason", "thomason_primary", "thomason_secondary",
"thomason_internal", "thomason_antisense",
"thomason_putative_asrna"
]
}
conditions = [
"signif_chimeras_of_iron_limitation_cl",
"signif_chimeras_of_log_phase_cl", "signif_chimeras_of_stationary_cl"
]
conditions_beauty_names = ["Iron limitation", "Log", "Stationary"]
short_name = {
"raghavan_s5": "R1",
"raghavan_s6": "R2",
"raghavan_s7": "R3",
"raghavan_2": "R4",
# "raghavan_s8": "R4",
"lybecker_s1": "L1",
"lybecker_s2": "L2",
"bilusic_s1": "B1",
"bilusic_s2": "B2",
"bilusic_s3_1": "B3_1",
"bilusic_s3_2": "B3_2",
"bilusic_s4_1": "B4_1",
"bilusic_s4_2": "B4_2",
# "zhang_s3_2013_sheet2008": "Z1",
# "zhang_s3_2013_sheet2009": "Z2",
# "zhang_s4_2013_sheet2008": "Z3",
# "zhang_s4_2013_sheet2009": "Z4",
"thomason": "T1",
"thomason_primary": "T1_1",
"thomason_secondary": "T1_2",
"thomason_internal": "T1_3",
"thomason_antisense": "T1_4",
"thomason_putative_asrna": "T1_5",
"mcdowell": "M1"
}
beauty_type_names = {
"3utr": "3UTR",
"5utr": "5UTR",
"as": "AS",
"cis_as_with_trans_t": "cASt",
"igr": "IGR",
"mrna": "CDS",
"other-ncrna": "oRNA",
"srna": "sRNA",
"trna": "tRNA",
"tu": "IGT"
}
loader = TableLoader()
results = loader.load(path)
header = [
"Name", "EcoCyc id", "Type", "Total UI", "sRNA UI", "CDS & 5'UTR UI",
"3'UTR & IGR UI"
]
start_of_total_interactions = len(header)
for cond_name in conditions_beauty_names:
header.append("TNR %s" % cond_name)
end_of_total_interactions = len(header)
start_of_interactions = end_of_total_interactions
for cond_name in conditions_beauty_names:
header.append("Fraction as RNA2 %s" % cond_name)
end_of_interactions = len(header)
header.extend([
"Longest U tract", "MEME E-value", "MAST P-value", "Meme motif",
"Total number of targets", "Number of targets with motif",
"Overlaps known binding site"
])
start_of_regular_tables = len(header)
for set_name in sets:
header.append(set_name)
end_of_regular_tables = len(header)
for set_name in thomason_set:
header.append(set_name)
end_of_thomason_tables = len(header)
for set_name in mcdowell_set:
header.append(set_name)
end_of_tables = len(header)
header.append("# of supporting papers")
final_rows = []
# Go over the rows and fill according to the header
for index, row in enumerate(results):
row_values = [
row["name"], row["ecocyc_id"], row["type"], row["total_targets"],
row["tb_srna_targets"], row["mrna_5utr_targets"],
row["igr_3utr_targets"]
]
# Total interactions
for field in header[
start_of_total_interactions:end_of_total_interactions]:
cond_name = get_condition_by_header_name(field, conditions,
conditions_beauty_names)
first_count = int(row["%s_first_interactions" % cond_name])
second_count = int(row["%s_second_interactions" % cond_name])
row_values.append(first_count + second_count)
# interactions percentage
for field in header[start_of_interactions:end_of_interactions]:
cond_name = get_condition_by_header_name(field, conditions,
conditions_beauty_names)
first_count = float(row["%s_first_interactions" % cond_name])
second_count = float(row["%s_second_interactions" % cond_name])
total_count = first_count + second_count
if float(total_count) == 0:
res = "-"
else:
res = second_count / total_count
res = "%.2f" % res
row_values.append(res)
row_values.extend([
row["max_poly_u_length"], row["meme"].upper(), row["mast"].upper(),
row["motif"], row["total_number_of_targets"],
row["number_of_targets_with_motif"], row["binding_site_state"]
])
total_articles = 0
# Go over the table hit fields and merge columns
for set_name in header[start_of_regular_tables:end_of_regular_tables]:
field_values = []
for cond_name in conditions:
for table in sets[set_name]:
if row["%s_%s" % (table, cond_name)] == "+":
field_values.append(short_name[table])
elif row["%s_%s" % (table, cond_name)] == "-":
continue
else:
print "[warning] invalid value for cell"
if len(field_values) > 0:
total_articles += 1
row_values.append(";".join(list(set(field_values))))
# Go over the table hit fields and merge columns - for thomason
for set_name in header[end_of_regular_tables:end_of_thomason_tables]:
field_values = []
for table in thomason_set[set_name]:
if row[table] == "+":
field_values.append(short_name[table])
elif row[table] == "-":
continue
else:
print "[warning] invalid value for cell"
if len(field_values) > 0:
total_articles += 1
row_values.append(";".join(list(set(field_values))))
# Go over the table hit fields and merge columns - for mcdowell
for set_name in header[end_of_thomason_tables:end_of_tables]:
field_values = []
for table in mcdowell_set[set_name]:
if row[table] == "+":
field_values.append(short_name[table])
elif row[table] == "-":
continue
else:
print "[warning] invalid value for cell"
if len(field_values) > 0:
total_articles += 1
row_values.append(";".join(list(set(field_values))))
row_values.append(total_articles)
final_rows.append(row_values)
# go over the rows and fix name, id, type notations
names = get_name_dictionary(our_tables)
ecocyc_ids = get_ecocyc_id_dictionary(our_tables)
for row in final_rows:
row[0] = names[row[0]]
row[1] = ecocyc_ids[row[1]]
row[2] = beauty_type_names[row[2]]
if row[1].split(".")[-1].isdigit():
row[1] = ".".join(row[1].split(".")[:-1])
if ".TU" in row[0]:
row[0] = row[0].replace(".TU", ".IGT")
if ".TU" in row[1]:
row[1] = row[1].replace(".TU", ".IGT")
with open(output_file, "wb") as fl:
fl.write("%s\n" % "\t".join(header))
for row in final_rows:
fl.write("%s\n" % "\t".join(str(val) for val in row))