def handle_input(filename):
sys.stdout.write("Handling %s\n" % filename)
not_significant_ids = IdList()
not_found_ids = IdList()
prefix = split_filename(filename)[1]
index_file = "%s.tmp.idx" % prefix
hmm_dict = SearchIO.index_db(index_file, filename, args.format)
if args.output == "stdout":
out_fd = sys.stdout
else:
out_fd = open("%s%s.top_hits" % (args.top_hits_dir, prefix), "w")
out_fd.write("#query\thit\tevalue\tbitscore\n")
for query in hmm_dict:
if hmm_dict[query].hits:
if hmm_dict[query][0].is_included:
out_fd.write(
"%s\t%s\t%s\t%s\n" %
(query, hmm_dict[query][0].id, hmm_dict[query][0].evalue,
hmm_dict[query][0].bitscore))
else:
not_significant_ids.append(query)
else:
not_found_ids.append(query)
if args.output != "stdout":
out_fd.close()
os.remove(index_file)
return not_significant_ids, not_found_ids
def prepare_annotation_file_from_transcript_and_cds(
self,
transcript_file,
cds_file,
correspondence_file,
output_prefix,
format="fasta",
correspondence_key_column=0,
correspondence_value_column=1,
verbose=False):
transcript_dict = self.parse_seq_file(transcript_file,
"parse",
format=format)
cds_dict = self.parse_seq_file(cds_file, "parse", format=format)
correspondence_dict = SynDict(filename=correspondence_file,
comments_prefix="#",
key_index=correspondence_key_column,
value_index=correspondence_value_column)
no_corresponding_cds_transcript_list = IdList()
cds_not_found_transcript_list = IdList()
annotation_file = "%s.annotation" % output_prefix
no_corresponding_cds_transcript_file = "%s.no_cds.id" % output_prefix
cds_not_found_transcript_file = "%s.not_found_cds.id" % output_prefix
with open(annotation_file, "w") as annotation_fd:
for transcript_id in transcript_dict:
if transcript_id not in correspondence_dict:
no_corresponding_cds_transcript_list.append(transcript_id)
if verbose:
print(
"No cds in correspondence file for transcript %s" %
transcript_id)
continue
cds_id = correspondence_dict[transcript_id]
length = len(cds_dict[cds_id].seq)
start = transcript_dict[transcript_id].seq.upper().find(
cds_dict[cds_id].seq.upper())
if start == -1:
cds_not_found_transcript_list.append(transcript_id)
if verbose:
print("CDS was not found for transcript %s" %
transcript_id)
continue
annotation_string = "%s\t+\t%i\t%i\n" % (transcript_id,
start + 1, length)
annotation_fd.write(annotation_string)
no_corresponding_cds_transcript_list.write(
no_corresponding_cds_transcript_file)
cds_not_found_transcript_list.write(cds_not_found_transcript_file)
def filter_psl_by_ids_from_file(self, psl_file, output_file,
white_query_id_file=None,
black_query_id_file=None,
white_target_id_file=None,
black_target_id_file=None):
self.filter_psl_by_ids(psl_file, output_file,
white_query_id_list=IdList(filename=white_query_id_file) if white_query_id_file else (),
black_query_id_list=IdList(filename=black_query_id_file) if black_query_id_file else (),
white_target_id_list=IdList(filename=white_target_id_file) if white_target_id_file else (),
black_target_id_list=IdList(filename=black_target_id_file) if black_target_id_file else ())
def extract_counts_by_max_level(input_file, output_prefix,
separator="\t", verbose=True):
output_file = "%s.divided_by_maxlvl" % output_prefix
zero_max_lvl_list = IdList()
zero_max_lvl_list_file = "%s.zero_max_lvl.ids" % output_prefix
with open(input_file, "r") as in_fd:
header = in_fd.readline()
header_list = header.strip().split(separator)
with open(output_file, "w") as out_fd:
out_fd.write(header)
for line in in_fd:
tmp_line = line.strip().split(separator)
data = np.array(map(float, tmp_line[1:]))
max_level = max(data)
if max_level == 0:
zero_max_lvl_list.append(tmp_line[0])
if verbose:
print("Zero max level for %s...Skipping..." % tmp_line[0])
continue
data /= max_level
output_string = tmp_line[0] + "\t"
output_string += "\t".join(map(str, data))
output_string += "\n"
out_fd.write(output_string)
zero_max_lvl_list.write(zero_max_lvl_list_file)
def add_length_to_accordance_file(accordance_file, length_file, output_prefix):
accordance_dict = SynDict(filename=accordance_file, allow_repeats_of_key=True)
length_dict = SynDict(filename=length_file, expression=int)
print length_dict
longest_list = IdList()
all_output_file = "%s.all.correspondence" % output_prefix
longest_output_file = "%s.longest.correspondence" % output_prefix
longest_id_file = "%s.longest.ids" % output_prefix
with open(all_output_file, "w") as all_out_fd:
with open(longest_output_file, "w") as longest_out_fd:
for gene in accordance_dict:
current_transcript = None
current_length = 0
for transcript in accordance_dict[gene]:
if length_dict[transcript] > current_length:
current_transcript = transcript
current_length = length_dict[transcript]
all_out_fd.write("%s\t%s\t%i\n" % (gene, transcript, length_dict[transcript]))
longest_out_fd.write("%s\t%s\t%i\n" % (gene, current_transcript, current_length))
longest_list.append(current_transcript)
longest_list.write(longest_id_file)
def combine_ds_dn_w_from_bootstrap_data(self, input_dir, output_dir, use_node_names_if_possible=True):
dn_dir = "%s/dN/" % output_dir
ds_dir = "%s/dS/" % output_dir
w_dir = "%s/W/" % output_dir
for directory in output_dir, dn_dir, ds_dir, w_dir:
self.safe_mkdir(directory)
input_files = map(lambda s: "%s/%s" % (input_dir, s), os.listdir(input_dir))
data_dict = OrderedDict()
for filename in input_files:
with open(filename, "r") as in_fd:
in_fd.readline() # read header
for line in in_fd:
node_id, node_name, dn, ds, w = line.strip().split("\t")
if use_node_names_if_possible:
node = node_id if node_name == "." else node_name
else:
node = node_id
if node not in data_dict:
data_dict[node] = OrderedDict()
for parameter in "dN", "dS", "W":
data_dict[node][parameter] = IdList()
data_dict[node]["dN"].append(dn)
data_dict[node]["dS"].append(ds)
data_dict[node]["W"].append(w)
for node in data_dict:
for parameter in "dN", "dS", "W":
out_file = "%s/%s/%s.tsv" % (output_dir, parameter, node)
data_dict[node][parameter].write(out_file)
def extract_clusters_by_element_ids_from_file(self,
cluster_file,
element_file,
output_file,
mode="w",
cluster_column=0,
element_column=1,
column_separator="\t",
element_separator=",",
id_column=None):
""""
mode: "w" - if elements from element_id_list are present in cluster extracts only that elements
"a" - if elements from element_id_list are present in cluster extracts all elements
"""
cluster_dict = SynDict(filename=cluster_file,
split_values=True,
comments_prefix="#",
key_index=cluster_column,
value_index=element_column,
separator=column_separator,
values_separator=element_separator)
element_id_list = IdList(filename=element_file,
comments_prefix="#",
column_number=id_column)
extracted_clusters = self.extract_clusters_by_element_ids(
cluster_dict, element_id_list, mode=mode)
extracted_clusters.write(output_file, splited_values=True)
def extract_annotation_by_refence_id(list_of_target_gff, id_file,
extracted_gff, filtered_out_gff):
ids = IdList()
ids.read(id_file)
extracted_gff_fd = open(extracted_gff, "w")
filtered_out_gff_fd = open(filtered_out_gff, "w")
for filename in list_of_target_gff:
with open(filename, "r") as in_fd:
for line in in_fd:
tmp = line
if tmp == "# --- START OF GFF DUMP ---\n":
# read until string with target_name will appear
while tmp[0] == "#":
tmp = next(in_fd, "")
target_name = tmp.split("\t")[8].split(
";")[1].split()[1]
if target_name not in ids:
writing_fd = filtered_out_gff_fd
else:
writing_fd = extracted_gff_fd
# print target_name
writing_fd.write(tmp)
while True:
tmp = next(in_fd, "")
if tmp == "# --- END OF GFF DUMP ---\n":
break
writing_fd.write(tmp)
if tmp == "":
break
extracted_gff_fd.close()
filtered_out_gff_fd.close()
def extract_top_hits_from_target_gff(list_of_target_gff,
top_hits_gff,
secondary_hits_gff,
id_white_list_file=None,
max_hits_per_query=None):
if id_white_list_file:
white_ids = IdList()
white_ids.read(id_white_list_file)
top_hits_gff_fd = open(top_hits_gff, "w")
secondary_hits_gff_fd = open(secondary_hits_gff, "w")
targets_list = []
hit_counter = 0
gene_counter = 0
for filename in list_of_target_gff:
index = 0
with open(filename, "r") as in_fd:
#print u
#tmp = None
for line in in_fd:
tmp = line
if tmp == "# --- START OF GFF DUMP ---\n":
# read until string with target_name will appear
while tmp[0] == "#":
tmp = next(in_fd, "")
target_name = tmp.split("\t")[8].split(
";")[1].split()[1]
if id_white_list_file:
if target_name not in white_ids:
continue
if target_name not in targets_list:
writing_fd = top_hits_gff_fd
targets_list.append(target_name)
gene_counter += 1
hit_counter = 0
else:
writing_fd = secondary_hits_gff_fd
# print target_name
hit_counter += 1
tmp = tmp.replace(
"gene_id 0",
"gene_id g%i_h%i" % (gene_counter, hit_counter))
if hit_counter <= max_hits_per_query:
writing_fd.write(tmp)
while True:
tmp = next(in_fd, "")
# print("cccc")
if tmp == "# --- END OF GFF DUMP ---\n":
break
if max_hits_per_query:
if hit_counter > max_hits_per_query:
#print "aaaaa"
continue
writing_fd.write(tmp)
if tmp == "":
break
top_hits_gff_fd.close()
secondary_hits_gff_fd.close()
def extract_entries_by_GO_from_eggnogmapper_output(
eggnogmapper_output,
GO_file,
output_prefix,
comments_prefix="#",
separator="\t",
):
GO_list = IdList(filename=GO_file, column_number=0)
#print "GOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO"
#print GO_list
print(len(GO_list))
extracted_entries_file = "%s.annotations" % output_prefix
extracted_entries = 0
with open(eggnogmapper_output, "r") as eggnog_fd:
with open(extracted_entries_file, "w") as out_fd:
for line in eggnog_fd:
if line[0] == comments_prefix:
out_fd.write(line)
continue
line_list = line.strip().split(separator)
entry_GO_list = line_list[5].split(",")
#print entry_GO_list
for GO in entry_GO_list:
if GO in GO_list:
out_fd.write(line)
extracted_entries += 1
break
print("Extracted %i entries" % extracted_entries)
def get_longest_pep_per_gene_from_ensembl_pep_dict(protein_dict, output_prefix=None):
length_file = "%s.protein_length.tsv" % output_prefix
if output_prefix:
longest_protein_id_file = "%s.longest_pep.ids" % output_prefix
len_fd = open(length_file, 'w')
len_fd.write("#gene_id\tprotein_id\tprotein_length\n")
data_dict = OrderedDict()
for protein_id in protein_dict:
length = len(protein_dict[protein_id].seq)
description_list = protein_dict[protein_id].description.split()
#print protein_dict[protein_id]
#print ''
#print description_list
for entry in description_list:
if "gene:" in entry:
gene_id = entry.split(":")[1]
if output_prefix:
len_fd.write("%s\t%s\t%i\n" % (gene_id, protein_id, length))
if gene_id not in data_dict:
data_dict[gene_id] = protein_id
else:
if length > len(protein_dict[data_dict[gene_id]].seq):
data_dict[gene_id] = protein_id
longest_pep_ids = IdList(data_dict.values())
if output_prefix:
longest_pep_ids.write(longest_protein_id_file)
len_fd.close()
return longest_pep_ids
def calculate_fpkm_for_count_table(count_table_file, transcript_length_file, output_file,
separator="\t"):
length_dict = SynDict(filename=transcript_length_file, expression=int, comments_prefix="#")
with open(count_table_file, "r") as in_fd:
header_list = in_fd.readline().strip().split(separator)
samples_list = header_list[1:]
gene_list = IdList()
count_list = []
for line in in_fd:
tmp = line.strip().split(separator)
gene_list.append(tmp[0])
count_list.append(map(float, tmp[1:]))
per_sample_total_counts = []
for sample_index in range(0, len(samples_list)):
total_counts = 0
for gene_index in range(0, len(count_list)):
total_counts += count_list[gene_index][sample_index]
per_sample_total_counts.append(total_counts)
with open(output_file, "w") as out_fd:
out_fd.write(separator.join(header_list) + "\n")
for gene_index in range(0, len(count_list)):
normalized_counts_list = []
for sample_index in range(0, len(samples_list)):
gene_count = count_list[gene_index][sample_index] * (10**9) / length_dict[gene_list[gene_index]] / per_sample_total_counts[sample_index]
normalized_counts_list.append(gene_count)
out_fd.write("%s\t%s\n" % (gene_list[gene_index], "\t".join(map(str, normalized_counts_list))))
def split_hmm(self, hmmfile, output_dir, num_of_recs_per_file, num_of_files=None, output_prefix=None, threads=4):
try:
os.mkdir(output_dir)
except OSError:
pass
id_fd = CGAS.cgas(hmmfile, grep_pattern="NAME", whole_word_match=True, awk_code="{print $2}",
capture_output=True)
split_index = 1
ids_written = 0
ids_list = IdList()
#ids_list = read_ids(id_fd, close_after_if_file_object=False)
ids_list.read(id_fd, close_after_if_file_object=True)
number_of_ids = len(ids_list)
out_prefix = self.split_filename(hmmfile)[1] if output_prefix is None else output_prefix
num_of_ids = int(number_of_ids/num_of_files) + 1 if num_of_files else num_of_recs_per_file
common_options = " -f"
common_options += " %s" % hmmfile
options_list = []
while (ids_written + num_of_ids) <= number_of_ids:
tmp_id_list = IdList(ids_list[ids_written:ids_written+num_of_ids])
tmp_id_list.write("%s/%s_%i.ids" % (output_dir, out_prefix, split_index))
options = common_options
options += " %s/%s_%i.ids" % (output_dir, out_prefix, split_index)
options += " > %s" % ("%s/%s_%i.hmm" % (output_dir, out_prefix, split_index))
options_list.append(options)
split_index += 1
ids_written += num_of_ids
if ids_written != number_of_ids:
tmp_id_list = IdList(ids_list[ids_written:])
tmp_id_list.write("%s/%s_%i.ids" % (output_dir, out_prefix, split_index))
options = common_options
options += " %s/%s_%i.ids" % (output_dir, out_prefix, split_index)
options += " > %s" % ("%s/%s_%i.hmm" % (output_dir, out_prefix, split_index))
options_list.append(options)
split_index += 1
#print options_list
self.parallel_execute(options_list, cmd="hmmfetch", threads=threads)
def extract_sequences_from_selected_clusters(
self,
clusters_id_file,
cluster_file,
seq_file,
output_dir="./",
seq_format="fasta",
out_prefix=None,
create_dir_for_each_cluster=False,
skip_cluster_if_no_sequence_for_element=True):
from Routines import SequenceRoutines, FileRoutines
cluster_id_list = IdList()
cluster_dict = SynDict()
#print(pep_file)
FileRoutines.safe_mkdir(output_dir)
out_dir = FileRoutines.check_path(output_dir)
create_directory_for_each_cluster = True if out_prefix else create_dir_for_each_cluster
if clusters_id_file:
cluster_id_list.read(clusters_id_file)
cluster_dict.read(cluster_file,
split_values=True,
values_separator=",")
protein_dict = SeqIO.index_db(
"tmp.idx",
FileRoutines.make_list_of_path_to_files(seq_file),
format=seq_format)
number_of_skipped_clusters = 0
for fam_id in cluster_id_list if clusters_id_file else cluster_dict:
if skip_cluster_if_no_sequence_for_element:
absent_elements = self.check_absence_of_cluster_elements(
cluster_dict[fam_id], protein_dict)
if absent_elements:
print "Skipping cluster %s due to absent element(%s)" % (
fam_id, ",".join(absent_elements))
number_of_skipped_clusters += 1
continue
if fam_id in cluster_dict:
if create_directory_for_each_cluster:
fam_dir = "%s%s/" % (out_dir, fam_id)
FileRoutines.safe_mkdir(fam_dir)
out_file = "%s%s.fasta" % (fam_dir, out_prefix
if out_prefix else fam_id)
else:
out_file = "%s/%s.fasta" % (out_dir, out_prefix
if out_prefix else fam_id)
SeqIO.write(SequenceRoutines.record_by_id_generator(
protein_dict, cluster_dict[fam_id], verbose=True),
out_file,
format=seq_format)
os.remove("tmp.idx")
print "%i of %i clusters were skipped due to absent elements" % (
number_of_skipped_clusters, len(cluster_dict))
return number_of_skipped_clusters
def create_per_cluster_element_id_files(self, cluster_dict,
output_directory):
self.safe_mkdir(output_directory)
for cluster_id in cluster_dict:
cluster_element_id_list = IdList(cluster_dict[cluster_id])
cluster_element_id_list.write("%s/%s.ids" %
(output_directory, cluster_id))
def extract_ids_from_file(input_file, output_file=None, header=False, column_separator="\t",
comments_prefix="#", column_number=None):
id_list = IdList()
id_list.read(input_file, column_separator=column_separator, comments_prefix=comments_prefix,
column_number=column_number, header=header)
if output_file:
id_list.write(output_file, header=header)
return id_list
def _get_tree_dist_values(tree, expression=None):
feature_values_list = IdList()
for node in tree.traverse():
if expression is not None:
if not expression(node):
continue
feature_values_list.append(node.dist)
return feature_values_list
def divide_counts_by_base_level(input_file, output_prefix, base_level,
separator="\t", verbose=True, secondary_base_lvl=None):
output_file = "%s.divided_by_baselvl" % output_prefix
zero_base_lvl_list = IdList()
zero_both_base_lvls_list = IdList()
zero_base_lvl_list_file = "%s.zero_base_lvl.ids" % output_prefix
zero_both_base_lvls_list_file = "%s.zero_base_lvls.ids" % output_prefix
with open(input_file, "r") as in_fd:
header = in_fd.readline()
header_list = header.strip().split(separator)
data_base_level_index = header_list.index(base_level) - 1
if secondary_base_lvl:
data_secondary_base_level_index = header_list.index(secondary_base_lvl) - 1
with open(output_file, "w") as out_fd:
out_fd.write(header)
for line in in_fd:
tmp_line = line.strip().split(separator)
data = np.array(map(float, tmp_line[1:]))
if data[data_base_level_index] == 0:
zero_base_lvl_list.append(tmp_line[0])
if not secondary_base_lvl:
if verbose:
print("Zero base level(%s) for %s...Skipping..." % (base_level, tmp_line[0]))
continue
if secondary_base_lvl:
if data[data_secondary_base_level_index] == 0:
zero_both_base_lvls_list.append(tmp_line[0])
if verbose:
print("Both base levels are zero (%s, %s) for %s...Skipping..." % (base_level,
secondary_base_lvl,
tmp_line[0]))
continue
data /= data[data_base_level_index] if data[data_base_level_index] != 0 else data[data_secondary_base_level_index]
else:
data /= data[data_base_level_index]
output_string = tmp_line[0] + "\t"
output_string += "\t".join(map(str, data))
output_string += "\n"
out_fd.write(output_string)
zero_base_lvl_list.write(zero_base_lvl_list_file)
zero_both_base_lvls_list.write(zero_both_base_lvls_list_file)
def extract_emapper_annotations_by_protein_ids(emapper_annotation_file,
protein_id_file,
output_annotations):
protein_ids = IdList(filename=protein_id_file)
with open(emapper_annotation_file, "r") as ann_fd:
with open(output_annotations, "w") as out_fd:
for line in ann_fd:
if line[0] == "#":
out_fd.write(line)
continue
if line.split("\t")[0] in protein_ids:
out_fd.write(line)
def cluster_sequence_names_by_id_fragment_from_file(
self,
seq_id_file,
id_element_index,
id_separator="_",
output_prefix=None):
seq_id_list = IdList(filename=seq_id_file)
self.cluster_sequence_names_by_id_fragment(seq_id_list,
id_element_index,
id_separator=id_separator,
output_prefix=output_prefix)
def extract_top_hits(hmmer_hits,
top_hits_file,
top_hits_ids_file=None,
not_significant_ids_file=None,
not_found_ids_file=None):
top_hits_ids = IdList()
not_significant_ids = IdList()
not_found_ids = IdList()
index_file = "hmmer_hits.tmp.idx"
hmm_dict = SearchIO.index_db(index_file, hmmer_hits, "hmmer3-text")
out_fd = open(top_hits_file, "w")
out_fd.write("#query\thit\tevalue\tbitscore\n")
for query in hmm_dict:
if hmm_dict[query].hits:
if hmm_dict[query][0].is_included:
out_fd.write("%s\t%s\t%s\t%s\n" %
(query, hmm_dict[query][0].id,
hmm_dict[query][0].evalue,
hmm_dict[query][0].bitscore))
top_hits_ids.append(query)
else:
not_significant_ids.append(query)
else:
not_found_ids.append(query)
os.remove(index_file)
if not_significant_ids_file:
not_significant_ids.write(not_significant_ids_file)
if not_found_ids_file:
not_found_ids.write(not_found_ids_file)
if top_hits_ids_file:
top_hits_ids.write(top_hits_ids_file)
def remove_elements_by_ids_from_files(self,
input_file,
output_file,
black_list_file,
mode="full"):
cluster_dict = SynDict(filename=input_file, split_values=True)
black_list = IdList(filename=black_list_file)
filtered_dict = self.remove_elements_by_ids(cluster_dict,
black_list,
mode=mode)
filtered_dict.write(output_file, splited_values=True)
def extract_top_hits(self, hmmer_hits, output_prefix, parsing_mode="index_db"): #top_hits_file, top_hits_ids_file=None,not_significant_ids_file=None, not_found_ids_file=None):
top_hits_ids = IdList()
not_significant_ids = IdList()
not_found_ids = IdList()
top_hits_file = "%s.top_hits" % output_prefix
top_hits_ids_file = "%s.top_hits.ids" % output_prefix
not_significant_ids_file = "%s.not_significant.ids" % output_prefix
not_found_ids_file = "%s.not_found.ids" % output_prefix
index_file = "%s.hmmer_hits.tmp.idx" % output_prefix
#hmm_dict = SearchIO.index_db(index_file, hmmer_hits, "hmmer3-text")
hmm_dict = self.parse_search_file(hmmer_hits, parsing_mode, format="hmmer3-text", index_file=None)
with open(top_hits_file, "w") as out_fd:
out_fd.write("#query\thit\tevalue\tbitscore\n")
for query in hmm_dict:
if hmm_dict[query].hits:
if hmm_dict[query][0].is_included:
out_fd.write("%s\t%s\t%s\t%s\n" % (query, hmm_dict[query][0].id, hmm_dict[query][0].evalue,
hmm_dict[query][0].bitscore))
top_hits_ids.append(query)
else:
not_significant_ids.append(query)
else:
not_found_ids.append(query)
if parsing_mode == "index_db":
os.remove(index_file)
for id_list, id_file in zip([not_significant_ids, not_found_ids, top_hits_ids],
[not_significant_ids_file, not_found_ids_file, top_hits_ids_file]):
id_list.write(id_file)
def convert_gff_to_simple_bed(input_gff, output_bed, feature_type_list=[], scaffold_id_file=None):
if scaffold_id_file:
scaffolds_id_list = IdList(filename=scaffold_id_file)
with open(input_gff, "r") as gff_fd:
with open(output_bed, "w") as bed_fd:
for line in gff_fd:
if line[0] == "#":
continue
tmp_list = line.strip().split("\t")
if scaffold_id_file:
if tmp_list[0] not in scaffolds_id_list:
continue
if feature_type_list:
if tmp_list[2] not in feature_type_list:
continue
bed_fd.write("%s\t%s\t%s\n" % (tmp_list[0], tmp_list[3], tmp_list[4]))
def extract_clusters_by_element_ids_from_file(self,
cluster_file,
element_file,
output_file,
mode="w"):
""""
mode: "w" - if elements from element_id_list are present in cluster extracts only that elements
"a" - if elements from element_id_list are present in cluster extracts all elements
"""
cluster_dict = SynDict()
cluster_dict.read(cluster_file, split_values=True, comments_prefix="#")
element_id_list = IdList()
element_id_list.read(element_file, comments_prefix="#")
extracted_clusters = self.extract_clusters_by_element_ids(
cluster_dict, element_id_list, mode=mode)
extracted_clusters.write(output_file, splited_values=True)
def extract_evidence_by_ids(evidence_file, id_file, output_evidence_file, mode="transcript"):
# possible modes: transcript, gene
ids = IdList()
ids.read(id_file, comments_prefix="#")
column_id = 0 if mode == "gene" else 1
with open(evidence_file, "r") as ev_fd:
with open(output_evidence_file, "w") as out_fd:
for line in ev_fd:
if line[0] == "#":
out_fd.write(line)
continue
entry_id = line.split("\t")[column_id]
if entry_id in ids:
out_fd.write(line)
def extract_clusters_and_elements_by_labels_from_files(
self,
cluster_file,
label_file,
output_file,
separator="@",
label_position="first"):
cluster_dict = SynDict(filename=cluster_file, split_values=True)
label_list = IdList(
filename=label_file) if isinstance(label_file, str) else label_file
output_dict = self.extract_clusters_and_elements_by_labels(
cluster_dict,
label_list,
separator=separator,
label_position=label_position)
output_dict.write(output_file, splited_values=True)
def divide_counts_by_several_base_level(input_file, output_prefix, base_levels,
separator="\t", verbose=True,
max_ratio_to_base_lvl=0.5):
output_file = "%s.divided_by_max_baselvl" % output_prefix
max_ratio_to_base_lvl_file = "%s.divided_by_max_baselvl.max_%f_ratio" % (output_prefix, max_ratio_to_base_lvl)
zero_max_base_lvl_list = IdList()
zero_max_base_lvl_list_file = "%s.zero_base_lvls.ids" % output_prefix
max_ratio_to_base_lvl_fd = open(max_ratio_to_base_lvl_file, "w")
with open(input_file, "r") as in_fd:
header = in_fd.readline()
header_list = header.strip().split(separator)
data_base_lvl_index_list = []
base_level_list = [base_levels] if isinstance(base_levels, str) else base_levels
for level in base_level_list:
data_base_lvl_index_list.append(header_list.index(level) - 1)
with open(output_file, "w") as out_fd:
out_fd.write(header)
max_ratio_to_base_lvl_fd.write(header)
for line in in_fd:
tmp_line = line.strip().split(separator)
data = np.array(map(float, tmp_line[1:]))
max_base_lvl = max(np.take(data, data_base_lvl_index_list))
if max_base_lvl == 0:
zero_max_base_lvl_list.append(tmp_line[0])
if verbose:
print("Zero max base level(s) for %s...Skipping..." % tmp_line[0])
continue
data /= max_base_lvl
output_string = tmp_line[0] + "\t"
output_string += "\t".join(map(str, data))
output_string += "\n"
if max(np.delete(data, data_base_lvl_index_list)) <= max_ratio_to_base_lvl:
max_ratio_to_base_lvl_fd.write(output_string)
out_fd.write(output_string)
zero_max_base_lvl_list.write(zero_max_base_lvl_list_file)
max_ratio_to_base_lvl_fd.close()
def extract_proteins_from_selected_families(
families_id_file,
fam_file,
pep_file,
output_dir="./",
pep_format="fasta",
out_prefix=None,
create_dir_for_each_family=False):
from Routines import SequenceRoutines, FileRoutines
fam_id_list = IdList()
fam_dict = SynDict()
#print(pep_file)
FileRoutines.safe_mkdir(output_dir)
out_dir = FileRoutines.check_path(output_dir)
create_directory_for_each_family = True if out_prefix else create_dir_for_each_family
if families_id_file:
fam_id_list.read(families_id_file)
fam_dict.read(fam_file, split_values=True, values_separator=",")
protein_dict = SeqIO.index_db("tmp.idx", pep_file, format=pep_format)
for fam_id in fam_id_list if families_id_file else fam_dict:
if fam_id in fam_dict:
if create_directory_for_each_family:
fam_dir = "%s%s/" % (out_dir, fam_id)
FileRoutines.safe_mkdir(fam_dir)
out_file = "%s%s.pep" % (fam_dir, out_prefix
if out_prefix else fam_id)
else:
out_file = "%s/%s.pep" % (out_dir, out_prefix
if out_prefix else fam_id)
SeqIO.write(SequenceRoutines.record_by_id_generator(
protein_dict, fam_dict[fam_id], verbose=True),
out_file,
format=pep_format)
else:
print("%s was not found" % fam_id)
os.remove("tmp.idx")
def extract_eggnog_fam_by_protein_syn_dict(self, eggnog_fam_dict, protein_syn_dict, output_prefix=None, species_id=None):
extracted_families = SynDict()
common_protein_names_to_families_dict = SynDict()
common_names_to_eggnog_proteins_syn_dict = SynDict()
not_found_proteins_common_names = IdList()
transposed_eggnog_fam_dict = eggnog_fam_dict.exchange_key_and_value()
for common_protein_name in protein_syn_dict:
not_found = True
for protein_id in protein_syn_dict[common_protein_name]:
extended_protein_id = protein_id if species_id is None else species_id + "." + protein_id
if extended_protein_id in transposed_eggnog_fam_dict:
not_found = False
if common_protein_name not in common_protein_names_to_families_dict:
common_protein_names_to_families_dict[common_protein_name] = [transposed_eggnog_fam_dict[extended_protein_id][0]]
common_names_to_eggnog_proteins_syn_dict[common_protein_name] = [extended_protein_id]
else:
common_protein_names_to_families_dict[common_protein_name].append(transposed_eggnog_fam_dict[extended_protein_id][0])
common_names_to_eggnog_proteins_syn_dict[common_protein_name].append(extended_protein_id)
if transposed_eggnog_fam_dict[extended_protein_id][0] not in extracted_families:
extracted_families[transposed_eggnog_fam_dict[extended_protein_id][0]] = eggnog_fam_dict[transposed_eggnog_fam_dict[extended_protein_id][0]]
if not_found:
not_found_proteins_common_names.append(common_protein_name)
if output_prefix:
extracted_families.write(filename="%s.extracted_families.fam" % output_prefix, splited_values=True)
common_protein_names_to_families_dict.write(filename="%s.common_protein_names_to_families.correspondence" % output_prefix, splited_values=True)
common_names_to_eggnog_proteins_syn_dict.write(filename="%s.common_protein_names_to_eggnog_proteins.correspondence" % output_prefix, splited_values=True)
not_found_proteins_common_names.write(filename="%s.not_found.common_names" % output_prefix)
#print common_names_to_eggnog_proteins_syn_dict
#print common_protein_names_to_families_dict
return extracted_families, common_protein_names_to_families_dict, \
common_names_to_eggnog_proteins_syn_dict, not_found_proteins_common_names