def create_annotations_table(annotations, output_directory, header,
schema_name, loci_info):
""" Creates output table with loci information.
Parameters
----------
annotations : dcit
Dictionary with loci identifiers as keys and
lists with information about loci as values (each
list contains the information extracted from the
"cds_info.tsv" table, if it was passed to the process,
and the product and URL link for the match found
through UniProt's SPARQL endpoint).
output_directory : str
Path to the output directory where the table
will be written to.
header : list
File header (first line with column names).
schema_name : str
Name of the schema.
loci_info : bool
True if the user passed the "cds_info.tsv" table
to the process, false otherwise.
Returns
-------
output_table : str
Path to the table with loci information.
"""
new_lines = [header]
for locus, data in annotations.items():
new_line = [locus]
if loci_info is True:
new_line += data[1:9]
else:
new_line += data[7:9]
if len(data[-1]) > 0:
relevant_data = [d[4:] + [str(round(d[3], 2))] for d in data[-1]]
proteome_data = list(zip(*relevant_data))
proteome_data = [
';'.join(list(map(str, d))) for d in proteome_data
]
proteome_data = [
'' if set(d) == {';'} else d for d in proteome_data
]
new_line.extend(proteome_data)
new_lines.append(new_line)
new_lines = ['\t'.join(l) for l in new_lines]
table_text = '\n'.join(new_lines)
table_basename = '{0}_annotations.tsv'.format(schema_name)
output_table = fo.join_paths(output_directory, [table_basename])
with open(output_table, 'w') as outfile:
outfile.write(table_text + '\n')
return output_table
def get_self_scores(fasta_file, output_directory, blast_threads,
blastp_path, makeblastdb_path):
""" Aligns a set of sequences against itself to determine
the raw score of the self-alignment.
Parameters
----------
fasta_file : str
Path to a FASTA file with protein sequences.
output_directory : str
Path to the directory where intermediate files
will be created.
blast_threads : int
Number of threads for BLASTp execution.
blastp_path : str
Path to the BLASTp executable.
makeblastdb_path : str
Path to the makeblastdb executable.
Returns
-------
self_lines_ids : dict
Dictionary with sequences identifiers as keys
and the BLASTp raw score from self-alignment.
"""
basename = fo.file_basename(fasta_file, suffix=False)
integer_seqids = fo.join_paths(output_directory,
['{0}_int.fasta'.format(basename)])
ids_dict = integer_headers(fasta_file, integer_seqids)
blastdb = fo.join_paths(output_directory, ['{0}_db'.format(basename)])
stderr = bw.make_blast_db(makeblastdb_path, integer_seqids,
blastdb, 'prot')
blastout = fo.join_paths(output_directory, ['self_blastout.tsv'])
self_results = bw.run_blast(blastp_path, blastdb, integer_seqids,
blastout, threads=blast_threads,
max_targets=1)
self_lines = fo.read_tabular(blastout)
self_lines_ids = {ids_dict[l[0]]: l[-1] for l in self_lines}
return self_lines_ids
def cds_batch_extractor(genomes, prodigal_path, temp_directory, index):
""" Extracts coding sequences from a set of genomes.
Parameters
----------
input_data : list
List with a set of paths for FASTA files with
genomic sequences, followed by the path to the
directory with files with Prodigal resutls, the
path to the temporary directory for all files and
directories that will be read and written and
an index/identifier to add to the output files
with coding sequences and coding sequences info.
Returns
-------
A list with the following elements:
protein_table : str
Path to the TSV file to which coding sequences
info was written.
cds_file : str
Path to the FASTA file to which coding sequences
were written.
batch_total : int
Total number of coding sequences extracted from
the set of input genomes.
"""
protein_table = fo.join_paths(temp_directory,
['protein_info_{0}.tsv'.format(index)])
cds_file = fo.join_paths(temp_directory,
['coding_sequences_{0}.fasta'.format(index)])
batch_total = 0
for g in genomes:
# determine Prodigal ORF file path for current genome
identifier = fo.file_basename(g, False)
orf_file_path = fo.join_paths(prodigal_path,
['{0}_ORF.txt'.format(identifier)])
total = save_extracted_cds(g, identifier, orf_file_path, protein_table,
cds_file)
batch_total += total
return [protein_table, cds_file, batch_total]
def get_proteomes(proteome_ids, output_dir):
""" Downloads reference proteomes from UniProt's FTP.
Parameters
----------
proteomes : list
List with a sublist per proteome to download.
Each sublist has the information about a proteome
that was contained in the README file with the list
of UniProt's reference proteomes.
output_dir : str
Path to the output directory where downloaded
proteomes will be saved to.
Returns
-------
Local paths to the downloaded proteomes.
"""
print('Downloading reference proteomes...')
# construct FTP URLs for each proteome
downloaded = 0
proteomes_files = []
for pid in proteome_ids:
domain = '{0}{1}'.format(pid[3][0].upper(), pid[3][1:])
proteome_id = '{0}_{1}'.format(pid[0], pid[1])
proteome_file = '{0}.fasta.gz'.format(proteome_id)
local_proteome_file = fo.join_paths(output_dir, [proteome_file])
proteome_url = fo.join_paths(ct.UNIPROT_PROTEOMES_FTP, [domain, pid[0], proteome_file])
res = fo.download_file(proteome_url, local_proteome_file)
proteomes_files.append(local_proteome_file)
downloaded += 1
print('\r', 'Downloaded {0}/{1}'.format(downloaded, len(proteome_ids)), end='')
time.sleep(0.1)
return proteomes_files
def split_fasta(fasta_path, output_path, num_seqs, filenames):
""" Splits a FASTA file.
Parameters
----------
fasta_path : str
Path to a FASTA file.
output_path : str
Path to the output directory where new FASTA
files will be created.
num_seqs : int
Split FASTA file into files with this number
of sequences.
filenames : gen
Generator with names to attribute to new files.
Returns
-------
splitted_files : list
List with paths to the new files that were
created by splitting the input FASTA file.
"""
splitted_files = []
current_recs = []
records = [rec for rec in SeqIO.parse(fasta_path, 'fasta')]
for record in records:
current_recs.append(record)
if len(current_recs) == num_seqs or record.id == records[-1].id:
file_name = filenames.__next__()
file_name = im.replace_multiple_characters(file_name, ct.CHAR_REPLACEMENTS)
new_file = fo.join_paths(output_path,
['{0}{1}'.format(file_name, '.fasta')])
splitted_files.append(new_file)
write_records(current_recs, new_file)
current_recs = []
return splitted_files
def translate_fastas(fasta_paths, output_directory, translation_table):
""" Translates DNA sequences in a set of FASTA files.
Parameters
----------
fasta_paths : list
List with the paths to the FASTA files that contain
the DNA sequences to translate.
output_directory : str
Path to the output directory where FASTA files with
protein sequences will be writen to.
translation_table : int
Genetic code used to translate DNA sequences.
Returns
-------
protein_files : list
List that contains the paths to the FASTA files with
translated sequences.
"""
protein_files = []
for path in fasta_paths:
records = import_sequences(path)
translated_records = {seqid: str(sm.translate_dna(seq, translation_table, 0)[0][0])
for seqid, seq in records.items()}
translated_lines = fasta_lines(list(translated_records.keys()),
translated_records)
basename = fo.file_basename(path).replace('.fasta', '_protein.fasta')
prot_file = fo.join_paths(output_directory, [basename])
fo.write_lines(translated_lines, prot_file)
protein_files.append(prot_file)
return protein_files
def write_gene_list(schema_dir):
""" Creates list with gene files in a schema and
uses the pickle module to save the list to a file.
Parameters
----------
schema_dir : str
Path to the directory with schema files.
Returns
-------
A list with two elements. A boolean value that
is True if the file with the list of genes was
created and False otherwise. The second element
is the path to the created file.
"""
schema_files = [
file for file in os.listdir(schema_dir) if '.fasta' in file
]
schema_list_file = fo.join_paths(schema_dir, ['.genes_list'])
fo.pickle_dumper(schema_files, schema_list_file)
return [os.path.isfile(schema_list_file), schema_list_file]
def main(input_files, output_directory, protein_table, blast_score_ratio,
cpu_cores, taxa, proteome_matches, no_cleanup, blast_path):
# create output directory
fo.create_directory(output_directory)
# create temp directory
temp_directory = fo.join_paths(output_directory, ['temp'])
fo.create_directory(temp_directory)
# validate input files
genes_list = fo.join_paths(temp_directory, ['listGenes.txt'])
genes_list = pv.check_input_type(input_files, genes_list)
loci_paths = fo.read_lines(genes_list)
schema_directory = os.path.dirname(loci_paths[0])
schema_basename = fo.file_basename(schema_directory)
print('Schema: {0}'.format(schema_directory))
print('Number of loci: {0}'.format(len(loci_paths)))
# find annotations based on reference proteomes for species
proteome_results = {}
if taxa is not None:
proteome_results = proteome_annotations(schema_directory,
temp_directory,
taxa,
blast_score_ratio,
cpu_cores,
proteome_matches,
blast_path)
# find annotations in SPARQL endpoint
print('\nQuerying UniProt\'s SPARQL endpoint...')
config_file = fo.join_paths(input_files, '.schema_config')
if os.path.isfile(config_file) is True:
config = fo.pickle_loader(config_file)
translation_table = config.get('translation_table', [11])[0]
else:
translation_table = 11
sparql_results = sparql_annotations(loci_paths,
translation_table,
cpu_cores)
loci_info = {}
if protein_table is not None:
# read cds_info table
# read "cds_info.tsv" file created by CreateSchema
table_lines = fo.read_tabular(protein_table)
for l in table_lines[1:]:
# create locus identifier based on genome identifier and
# cds identifier in file
locus_id = l[0].replace('_', '-')
locus_id = locus_id + '-protein{0}'.format(l[-2])
loci_info[locus_id] = l
annotations = join_annotations(sparql_results, proteome_results, loci_info)
# table header
header = ['Locus_ID']
if len(loci_info) > 0:
header += table_lines[0]
header += ['Uniprot_Name', 'UniProt_URL']
if len(proteome_results) > 0:
header.extend(['Proteome_ID', 'Proteome_Product',
'Proteome_Gene_Name', 'Proteome_Species',
'Proteome_BSR'])
loci_info_bool = True if len(loci_info) > 0 else False
output_table = create_annotations_table(annotations, output_directory,
header, schema_basename,
loci_info_bool)
if no_cleanup is False:
shutil.rmtree(temp_directory)
print('\n\nThe table with new information can be found at:'
'\n{0}'.format(output_table))
def proteome_annotations(schema_directory, temp_directory, taxa,
blast_score_ratio, cpu_cores, proteome_matches,
blast_path):
""" Determines loci annotations based on alignment against
UniProt's reference proteomes.
Parameters
----------
schema_directory : str
Path to the schema's directory.
temp_directory : str
Path to the temporary directory where intermediate
files will be written to.
taxa : list
List of taxa scientific names. The process will
search for reference proteomes whose "Species Name"
field contain any of the provided taxa names.
blast_score_ratio : float
BLAST Score Ratio value. Hits with a BSR value
>= than this value will be considered as high
scoring hits that can be included in the final
table according to the maximum number of matches
to report.
cpu_cores : int
Number of threads used to run BLASTp.
proteome_matches : int
Maximum number of proteome matches to report.
blast_path : str
Path to BLAST executables.
Returns
-------
proteome_results : dict
Dictionary with loci identifiers as keys and a list
with information about loci retrieved from the most
similar records in UniProt's reference proteomes.
"""
# get paths to files with representative sequences
short_directory = fo.join_paths(schema_directory, ['short'])
reps_paths = [fo.join_paths(short_directory, [file])
for file in os.listdir(short_directory)
if file.endswith('.fasta') is True]
print('Translating representative sequences...', end='')
# translate representatives for all loci
translated_reps = fo.join_paths(temp_directory, ['translated_reps'])
fo.create_directory(translated_reps)
reps_protein_files = fao.translate_fastas(reps_paths, translated_reps, 11)
print('done.')
print('Downloading list of reference proteomes...', end='')
remote_readme = fo.join_paths(ct.UNIPROT_PROTEOMES_FTP, ['README'])
local_readme = fo.join_paths(temp_directory,
['reference_proteomes_readme.txt'])
# get README file with list of reference proteomes
res = fo.download_file(remote_readme, local_readme)
print('done.')
# get lines with proteomes info for species of interest
readme_lines = fo.read_lines(local_readme, strip=False)
selected_proteomes = im.contained_terms(readme_lines, taxa)
selected_proteomes = [line.strip('\n') for line in selected_proteomes]
selected_proteomes = [line.split('\t') for line in selected_proteomes]
print('Found {0} reference proteomes for '
'{1}.'.format(len(selected_proteomes), taxa))
proteome_results = {}
if len(selected_proteomes) > 0:
# create directory to store proteomes
proteomes_directory = fo.join_paths(temp_directory, ['proteomes'])
fo.create_directory(proteomes_directory)
proteomes_files = ur.get_proteomes(selected_proteomes,
proteomes_directory)
# uncompress files and concatenate into single FASTA
uncompressed_proteomes = [fo.unzip_file(file) for file in proteomes_files]
proteomes_concat = fo.join_paths(proteomes_directory,
['full_proteome.fasta'])
proteomes_concat = fo.concatenate_files(uncompressed_proteomes,
proteomes_concat)
# get self-scores
# concatenate protein files
reps_concat = fo.concatenate_files(reps_protein_files,
fo.join_paths(temp_directory,
['reps_concat.fasta']))
print('\nDetermining self-score of representatives...', end='')
blastp_path = os.path.join(blast_path, ct.BLASTP_ALIAS)
makeblastdb_path = os.path.join(blast_path, ct.MAKEBLASTDB_ALIAS)
self_scores = fao.get_self_scores(reps_concat, temp_directory, cpu_cores,
blastp_path, makeblastdb_path)
print('done.')
# create BLASTdb with proteome sequences
proteome_blastdb = fo.join_paths(proteomes_directory,
['proteomes_db'])
stderr = bw.make_blast_db('makeblastdb', proteomes_concat,
proteome_blastdb, 'prot')
# BLASTp to determine annotations
blast_inputs = [['blastp', proteome_blastdb, file, file+'_blastout.tsv',
1, 1, None, None, proteome_matches, None, bw.run_blast]
for file in reps_protein_files]
print('\nBLASTing representatives against proteomes...')
blast_results = mo.map_async_parallelizer(blast_inputs,
mo.function_helper,
cpu_cores,
show_progress=True)
blastout_files = [fo.join_paths(translated_reps, [file])
for file in os.listdir(translated_reps)
if 'blastout' in file]
# index proteome file
indexed_proteome = SeqIO.index(proteomes_concat, 'fasta')
# process results for each BLASTp
proteome_results = extract_annotations(blastout_files,
indexed_proteome,
self_scores,
blast_score_ratio,
proteome_matches)
return proteome_results
def main(input_files, output_directory, cpu_cores, blast_score_ratio,
minimum_length, translation_table, ptf_path, size_threshold,
blast_path):
print('Adapting schema in the following '
'directory:\n{0}'.format(os.path.abspath(input_files)))
print('Prodigal training file:\n{0}'.format(ptf_path))
print('Number of cores: {0}'.format(cpu_cores))
print('BLAST Score Ratio: {0}'.format(blast_score_ratio))
print('Translation table: {0}'.format(translation_table))
print('Minimum accepted sequence length: {0}'.format(minimum_length))
print('Size threshold: {0}'.format(size_threshold))
# define output paths
schema_path = os.path.abspath(output_directory)
schema_short_path = fo.join_paths(schema_path, ['short'])
# create output directories
# check if they exist first
fo.create_directory(schema_path)
fo.create_directory(schema_short_path)
# list schema gene files
genes_file = pv.check_input_type(input_files,
os.path.join(output_directory, 'schema_genes.txt'))
# import list of schema files
with open(genes_file, 'r') as gf:
genes_list = [line.rstrip('\n') for line in gf]
os.remove(genes_file)
print('Number of genes to adapt: {0}\n'.format(len(genes_list)))
print('Determining the total number of alleles and '
'allele mean length per gene...\n'.format())
# count number of sequences and mean length per gene
genes_info = []
genes_pools = multiprocessing.Pool(processes=cpu_cores)
gp = genes_pools.map_async(fao.gene_seqs_info, genes_list,
callback=genes_info.extend)
gp.wait()
# split files according to number of sequences and sequence mean length
# in each file to pass even groups of sequences to all cores
even_genes_groups = mo.split_genes_by_core(genes_info, cpu_cores*4,
'seqcount')
# with few inputs, some sublists might be empty
even_genes_groups = [i for i in even_genes_groups if len(i) > 0]
# add common arguments
blastp_path = os.path.join(blast_path, ct.BLASTP_ALIAS)
makeblastdb_path = os.path.join(blast_path, ct.MAKEBLASTDB_ALIAS)
even_genes_groups = [[i, schema_path, schema_short_path,
blast_score_ratio, minimum_length,
translation_table, size_threshold,
blastp_path, makeblastdb_path,
adapt_loci] for i in even_genes_groups]
print('Adapting {0} genes...\n'.format(len(genes_list)))
invalid_data = mo.map_async_parallelizer(even_genes_groups,
mo.function_helper,
cpu_cores,
show_progress=True)
# define paths and write files with list of invalid
# alleles and invalid genes
output_schema_basename = os.path.basename(output_directory.rstrip('/'))
schema_parent_directory = os.path.dirname(schema_path)
# write file with alleles that were determined to be invalid
invalid_alleles = [sub[0] for sub in invalid_data]
invalid_alleles = list(itertools.chain.from_iterable(invalid_alleles))
invalid_alleles_file = os.path.join(schema_parent_directory,
'{0}_{1}'.format(output_schema_basename, 'invalid_alleles.txt'))
with open(invalid_alleles_file, 'w') as inv:
lines = ['{0}: {1}\n'.format(allele[0], allele[1]) for allele in invalid_alleles]
inv.writelines(lines)
# write file with identifiers of genes that had no valid alleles
invalid_genes = [sub[1] for sub in invalid_data]
invalid_genes = list(itertools.chain.from_iterable(invalid_genes))
invalid_genes_file = os.path.join(schema_parent_directory,
'{0}_{1}'.format(output_schema_basename, 'invalid_genes.txt'))
with open(invalid_genes_file, 'w') as inv:
invalid_geqids = '\n'.join(invalid_genes)
inv.write(invalid_geqids)
stats_lines = [sub[2] for sub in invalid_data]
stats_lines = list(itertools.chain.from_iterable(stats_lines))
stats_lines = ['\t'.join(line) for line in stats_lines]
stats_genes_file = '{0}/{1}_{2}'.format(schema_parent_directory,
output_schema_basename,
'summary_stats.txt')
with open(stats_genes_file, 'w') as stats:
summary_stats_text = '\n'.join(stats_lines)
stats.write('Gene\tTotal_alleles\tValid_alleles\tNumber_representatives\n')
stats.write(summary_stats_text)
print('\n\nNumber of invalid genes: {0}'.format(len(invalid_genes)))
print('Number of invalid alleles: {0}'.format(len(invalid_alleles)))
print('\nSuccessfully adapted {0}/{1} genes present in the '
'input schema.'.format(len(genes_list)-len(invalid_genes),
len(genes_list)))
def adapt_loci(genes, schema_path, schema_short_path, bsr, min_len,
table_id, size_threshold, blastp_path, makeblastdb_path):
""" Adapts a set of genes/loci from an external schema so that
that schema can be used with chewBBACA. Removes invalid alleles
and selects representative alleles to include in the "short" directory.
Parameters
----------
genes_list : list
A list with the following elements:
- List with paths to the files to be processed.
- Path to the schema directory.
- Path to the "short" directory.
- BLAST Score Ratio value.
- Minimum sequence length value.
- Genetic code.
- Sequence size variation threshold.
Returns
-------
invalid_alleles : list
List with the identifiers of the alleles that were
determined to be invalid.
invalid_genes : list
List with the identifiers of the genes that had no
valid alleles.
summary_stats : list of list
List with one sublist per processed locus. Each
sublist has four elements:
- The identifier of the locus.
- The number of alleles in the external file.
- The number of alleles that were a valid CDS.
- The number of representatives determined determined
by the process.
The function writes the schema files .
"""
# divide input list into variables
summary_stats = []
invalid_genes = []
invalid_alleles = []
for gene in genes:
representatives = []
final_representatives = []
# get gene basename and identifier
gene_basename = os.path.basename(gene)
gene_id = gene_basename.split('.f')[0]
# create paths to gene files in new schema
gene_file = fo.join_paths(schema_path,
['{0}{1}'.format(gene_id, '.fasta')])
gene_short_file = fo.join_paths(schema_short_path,
['{0}{1}'.format(gene_id, '_short.fasta')])
# create path to temp working directory for current gene
gene_temp_dir = fo.join_paths(schema_path,
['{0}{1}'.format(gene_id, '_temp')])
# create temp directory for the current gene
fo.create_directory(gene_temp_dir)
# dictionaries mapping gene identifiers to DNA sequences
# and Protein sequences
gene_seqs, prot_seqs, gene_invalid, seqids_map, total_sequences = \
sm.get_seqs_dicts(gene, gene_id, table_id, min_len, size_threshold)
invalid_alleles.extend(gene_invalid)
# if locus has no valid CDS sequences,
# continue to next locus
if len(prot_seqs) == 0:
shutil.rmtree(gene_temp_dir)
invalid_genes.append(gene_id)
summary_stats.append([gene_id, str(total_sequences), '0', '0'])
continue
if len(gene_seqs) > 1:
# identify DNA sequences that code for same protein
equal_prots = sm.determine_duplicated_seqs(prot_seqs)
# get only one identifier per protein
ids_to_blast = [protids[0] for protein, protids in equal_prots.items()]
# get longest sequence as first representative
longest = sm.determine_longest(ids_to_blast, prot_seqs)
representatives.append(longest)
final_representatives.append(longest)
# create FASTA file with distinct protein sequences
protein_file = fo.join_paths(gene_temp_dir,
['{0}_protein.fasta'.format(gene_id)])
protein_lines = fao.fasta_lines(ids_to_blast, prot_seqs)
fo.write_list(protein_lines, protein_file)
# create blastdb with all distinct proteins
blastp_db = os.path.join(gene_temp_dir, gene_id)
bw.make_blast_db(makeblastdb_path, protein_file, blastp_db, 'prot')
# determine appropriate blastp task (proteins < 30aa need blastp-short)
blastp_task = bw.determine_blast_task(equal_prots)
# cycles to BLAST representatives against non-representatives until
# all non-representatives have a representative
while len(set(ids_to_blast) - set(representatives)) != 0:
# create FASTA file with representative sequences
rep_file = fo.join_paths(gene_temp_dir,
['{0}_rep_protein.fasta'.format(gene_id)])
rep_protein_lines = fao.fasta_lines(representatives, prot_seqs)
fo.write_list(rep_protein_lines, rep_file)
# create file with seqids to BLAST against
ids_str = im.concatenate_list([str(i) for i in ids_to_blast], '\n')
ids_file = fo.join_paths(gene_temp_dir,
['{0}_ids.txt'.format(gene_id)])
fo.write_to_file(ids_str, ids_file, 'w', '')
# BLAST representatives against non-represented
blast_output = fo.join_paths(gene_temp_dir,
['{0}_blast_out.tsv'.format(gene_id)])
# set max_target_seqs to huge number because BLAST only
# returns 500 hits by default
blast_stderr = bw.run_blast(blastp_path, blastp_db, rep_file,
blast_output, 1, 1, ids_file,
blastp_task, 100000, ignore=ct.IGNORE_RAISED)
if len(blast_stderr) > 0:
raise ValueError(blast_stderr)
# import BLAST results
blast_results = fo.read_tabular(blast_output)
# get self-score for representatives
rep_self_scores = {res[1]: res[2] for res in blast_results
if res[0] == res[1]}
# divide results into high, low and hot BSR values
hitting_high, hitting_low, hotspots, high_reps, low_reps, hot_reps = \
bsr_categorizer(blast_results, representatives,
rep_self_scores, bsr, bsr+0.1)
excluded_reps = []
# remove high BSR hits that have representative
hitting_high = set(hitting_high)
ids_to_blast = [i for i in ids_to_blast if i not in hitting_high]
# remove representatives that led to high BSR with subjects that were removed
prunned_high_reps = {k: [r for r in v if r in ids_to_blast] for k, v in high_reps.items()}
reps_to_remove = [k for k, v in prunned_high_reps.items() if len(v) == 0]
excluded_reps.extend(reps_to_remove)
# determine smallest set of representatives that allow to get all cycle candidates
excluded = []
hotspot_reps = set(im.flatten_list(list(hot_reps.values())))
for rep, hits in hot_reps.items():
common = hotspot_reps.intersection(set(hits))
if len(common) > 0:
hotspot_reps = hotspot_reps - common
else:
excluded.append(rep)
excluded_reps.extend(excluded)
# remove representatives that only led to low BSR
excluded_reps.extend(low_reps)
representatives = [rep for rep in representatives if rep not in excluded_reps]
ids_to_blast = [i for i in ids_to_blast if i not in excluded_reps]
# determine next representative from candidates
rep_candidates = list(set(hotspots) - hitting_high)
# sort to guarantee reproducible results with same datasets
rep_candidates = sorted(rep_candidates, key=lambda x: int(x))
representatives, final_representatives = select_candidate(rep_candidates,
prot_seqs,
ids_to_blast,
representatives,
final_representatives)
# remove files created for current gene iteration
os.remove(rep_file)
os.remove(blast_output)
os.remove(ids_file)
else:
final_representatives = list(prot_seqs.keys())
# write schema file with all alleles
gene_lines = fao.fasta_lines(list(gene_seqs.keys()), gene_seqs)
fo.write_list(gene_lines, gene_file)
# get total number of valid sequences
valid_sequences = len(gene_lines)
# write schema file with representatives
final_representatives = [seqids_map[rep] for rep in final_representatives]
gene_rep_lines = fao.fasta_lines(final_representatives, gene_seqs)
fo.write_list(gene_rep_lines, gene_short_file)
# get number of representatives
representatives_number = len(gene_rep_lines)
summary_stats.append([gene_id,
str(total_sequences),
str(valid_sequences),
str(representatives_number)])
shutil.rmtree(gene_temp_dir)
return [invalid_alleles, invalid_genes, summary_stats]