def get_all_gene_names(in_files):
"""
Input: path to a folder with htseq-count output files
Output: list of unique gene names in input files
"""
genes_list = list()
for file_counter, in_file in enumerate(in_files):
in_data = hpf.l(in_file)
file_gene_counter = 0
for line in in_data:
if line[0:2] != "__":
split_line = line.split("\t")
gene_name = split_line[0]
if gene_name not in genes_list:
if file_counter == 0:
genes_list.append(gene_name)
else:
sys.stderr.write(
"Error: gene name " + gene_name + " in file " +
in_file +
" mismatches gene names in previously read file(s) in the same folder\n"
)
sys.exit(1)
file_gene_counter += 1
if file_gene_counter != len(genes_list):
sys.stderr.write(
"Error: mismatch in the number of genes between input files (encountered when reading "
+ in_file + ")\n")
sys.exit(1)
return genes_list
def main(input_vcf_file, out_path, window_step, exclude_samples):
exclude_list = list()
if exclude_samples != None:
exclude_list = exclude_samples.split(" ")
in_data = hpf.l(input_vcf_file)
in_data = [n for n in in_data if n.startswith("#") == False or n.startswith("#CHROM") == True]
header = in_data[0]
split_header = header.split("\t")
sample_names = split_header[9:len(split_header)]
samples_count = len(sample_names)
counts_dict = defaultdict(dict)
in_data = in_data[1:len(in_data)]
for line in in_data:
split_line = line.split()
sample_entries = split_line[9: len(split_line)]
coord = int(split_line[1])
bin = math.floor(coord / window_step)
bin = "bin_" + str(bin).zfill(3)
for i in range(0, samples_count):
sample_name = sample_names[i]
if sample_name not in exclude_list:
sample_entry = sample_entries[i]
if sample_name not in counts_dict[bin]:
counts_dict[bin][sample_name] = 0
if sample_entry != ".:0,0:0:.:0,0":
allele = sample_entry.split(":")[0]
if allele != "0" and allele != ".":
counts_dict[bin][sample_name] += 1
df = pd.DataFrame(counts_dict)
df.to_csv(out_path)
def main(in_folder, out_path):
in_files = hpf.get_file_paths(in_folder, "txt")
if len(in_files) == 0:
sys.stderr.write(
"Error: no files with .txt extension was found in the input folder\n"
)
sys.exit(1)
genes_list = get_all_gene_names(in_files)
collection_dict = dict()
for in_file in in_files:
genes_dict = dict()
sample_name = get_sample_name_from_file_name(in_file)
for item in genes_list:
genes_dict[item] = 0
in_data = hpf.l(in_file)
for line in in_data:
if line[0:2] != "__":
split_line = line.split("\t")
count = int(split_line[1])
gene_name = split_line[0]
genes_dict[gene_name] = count
collection_dict[sample_name] = genes_dict
collection_df = pd.DataFrame(collection_dict)
collection_df.to_csv(out_path, sep="\t", quoting=csv.QUOTE_NONE)
def load_dn_data(dn_path):
"""
Loads the dN results table
"""
dn_data = hpf.l(dn_path)
dn_data = dn_data[1: len(dn_data)]
dn_data = [n for n in dn_data if n.split()[1] != "NA"]
return dn_data
def write_temp_alignment_file(alignment_file_path, temp_folder):
"""
Writes an alignment file to the temporary files folder
"""
fasta_data = hpf.l(alignment_file_path)
out_file_path = temp_folder + "/temp_seq.fa"
with open(out_file_path, "w") as out_file:
for line in fasta_data:
if line.startswith(">"):
line = line.split("_")[0]
out_file.write(line + "\n")
def main(input_vcf_file, genome_size, exclude_samples):
exclude_list = list()
if exclude_samples != None:
exclude_list = exclude_samples.split(" ")
in_data = hpf.l(input_vcf_file)
in_data = [
n for n in in_data
if n.startswith("#") == False or n.startswith("#CHROM") == True
]
header = in_data[0]
split_header = header.split("\t")
sample_names = split_header[9:len(split_header)]
samples_count = len(sample_names)
counts_dict = dict()
for sample_name in sample_names:
counts_dict[sample_name] = 0
in_data = in_data[1:len(in_data)]
print("Sample\tMean_nr_of_variants_per_10_kb")
for line in in_data:
split_line = line.split()
sample_entries = split_line[9:len(split_line)]
for i in range(0, samples_count):
sample_name = sample_names[i]
sample_entry = sample_entries[i]
if sample_entry != ".:0,0:0:.:0,0":
allele = sample_entry.split(":")[0]
if allele != "0" and allele != ".":
counts_dict[sample_name] += 1
freq_list = list()
for sample in counts_dict:
if sample not in exclude_list:
sample_count = counts_dict[sample]
sample_freq = sample_count / (genome_size / 10000)
print(sample + "\t" + str(sample_freq))
freq_list.append(sample_freq)
print("-----")
print("Averaged numbers across all samples")
print("Mean number of variants per 10 kb:", np.mean(freq_list))
print("Median number of variants per 10 kb:", np.median(freq_list))
print("Standard deviation of the number of variants per 10 kb:",
np.std(freq_list))
def main(in_path, out_folder, fasta_path, deselected_scaffolds_path):
coords_df = None
deselected_scaffolds = []
if fasta_path == "":
coords_df = get_scaffold_coords_by_source_features(in_path)
else:
coords_df = get_scaffold_coords_by_fasta(in_path, fasta_path)
if deselected_scaffolds_path != "":
deselected_scaffolds = hpf.l(deselected_scaffolds_path)
records = list(SeqIO.parse(in_path, "embl"))
t = os.system("mkdir -p " + out_folder)
if t != 0:
sys.stderr.write(
"Error occurred when checking for the presence of output folder or creating the output folder ()"
+ out_folder + ")\n")
sys.exit(1)
for selected_scaff in range(0, coords_df.shape[0]):
coords_df_entry = coords_df.iloc[selected_scaff]
scaff_name = coords_df_entry["header"]
scaff_id = coords_df_entry["id"]
my_sequence_record = None
if scaff_name not in deselected_scaffolds:
query_start_coord = int(coords_df_entry.start_coord)
query_end_coord = int(coords_df_entry.end_coord)
out_path = out_folder + "/" + scaff_id + ".embl"
union_seq = str(records[0].seq)
seq = union_seq[query_start_coord - 1:query_end_coord - 1]
my_sequence = Seq(seq)
my_sequence_record = SeqRecord(my_sequence,
id=scaff_id,
name=scaff_name,
description="unknown_description",
dbxrefs=[])
my_sequence_record.seq.alphabet = generic_dna
my_sequence_record.accession = "unknown_accession"
my_sequence_record = process_record_features(
records, coords_df, query_start_coord, query_end_coord,
my_sequence_record)
SeqIO.write(my_sequence_record, out_path, "embl")
def load_pfam_domains(pfam_domains_path):
"""
Loads a tab separated table where column 1 contains Hepatocystis gene names and column 2 contains the PFAM domains in the corresponding genes
"""
domains_list = list()
domains_data = hpf.l(pfam_domains_path)
domains_data = domains_data[1: len(domains_data)]
pfam_domains_dict = dict()
for line in domains_data:
split_line = line.split()
pfam_domain_entry = split_line[1]
if pfam_domain_entry != "NA":
pfam_domains = pfam_domain_entry.split(",")
pfam_domains_dict[split_line[0]] = pfam_domains
for pfam_domain in pfam_domains:
if pfam_domain not in domains_list:
domains_list.append(pfam_domain)
return domains_list, pfam_domains_dict
def main(alignments_folder, output_folder, temp_folder, treefile_path):
treefile_content = hpf.l(treefile_path)
os.system("mkdir -p " + temp_folder)
os.system("mkdir -p " + output_folder)
os.chdir(temp_folder)
alignment_files = os.listdir(alignments_folder)
for alignment_file in alignment_files:
alignment_file_path = alignments_folder + "/" + alignment_file
write_temp_alignment_file(alignment_file_path, temp_folder)
write_codeml_ctl_file("temp_seq.fa", "temp_tree.treefile",
"temp_out.txt", temp_folder)
write_temp_treefile(treefile_content, temp_folder)
os.system("codeml codeml.ctl")
results_file_name = alignment_file.split(".fa")[0] + "_codeml.txt"
results_file_path = output_folder + "/" + results_file_name
os.system("cp " + temp_folder + "/temp_out.txt" + " " +
results_file_path)
def extract_sequences_from_fasta_by_id(args):
"""
Function for extracting sequences from a FASTA file by their names. The sequence names are truncated at the first space character
before they are compared to the query string. The function allows extracting sequences based on 1 query string and also reading a list of
query strings from a text file. There is an 'invert' mode to extract all sequences that do not match the query string(s).
"""
selected_seq_list = None
if args.string_query == True:
selected_seq_list = [args.query]
else:
selected_seq_list = hpf.l(args.query)
for header, seq in args.fasta_data:
fasta_seq_id = header.split()[0]
seq_to_output = False
if args.invert == False:
if fasta_seq_id in selected_seq_list:
seq_to_output = True
else:
if fasta_seq_id not in selected_seq_list:
seq_to_output = True
if seq_to_output == True:
print_header_and_seq(header, seq)