def qsub_to_check_matrix(config_dict, args):
snpdb = SNPdb(config_dict)
snpdb.parse_config_dict(config_dict)
snpdb._connect_to_snpdb()
snp_co = '1000000'
added_list = []
with open(args.added_list) as fi:
for x in fi.readlines():
added_list.append(x.strip())
present_strains = []
with open(args.present_strains) as fi:
for x in fi.readlines():
present_strains.append(x.strip())
strain_list = list(set(present_strains) | set(added_list))
snpdb.parse_args_for_update_matrix(snp_co, strain_list)
snpdb.check_matrix(strain_list, added_list)
def accept_outlier(args, config_dict):
#set up logging
logger = logging.getLogger('snapperdb.snpdb.export_json')
logger.info('Inititialising SnpDB Class')
#initalise snpdb class
snpdb = SNPdb(config_dict)
#parse confif
snpdb.parse_config_dict(config_dict)
#connect to postgresdb
snpdb._connect_to_snpdb()
snpdb.snpdb_conn = psycopg2.connect(snpdb.conn_string)
#remove isolate
snpdb.zscore_exception(args.out_strain)
def get_strains(args, config_dict):
#set up logging
logger = logging.getLogger('snapperdb.snpdb.get_strains')
logger.info('Inititialising SnpDB Class')
#initalise snpdb class
snpdb = SNPdb(config_dict)
#parse confif
snpdb.parse_config_dict(config_dict)
#connect to postgresdb
snpdb._connect_to_snpdb()
snpdb.snpdb_conn = psycopg2.connect(snpdb.conn_string)
#remove isolate
snpdb.get_strain_list(args.thresh)
def add_ref_cluster(args, config_dict):
#set up loggging
logger = logging.getLogger('snapperdb.snpdb.add_ref_cluster')
logger.info('Initialising SNPdb class')
#create snpdb class
snpdb = SNPdb(config_dict)
#parse config into snpdb object
logger.info('Parsing config dict')
snpdb.parse_config_dict(config_dict)
#connect to snpdb postgres
snpdb._connect_to_snpdb()
snpdb.snpdb_conn = psycopg2.connect(snpdb.conn_string)
snpdb.add_cluster()
def export_json(args, config_dict):
#set up logging
logger = logging.getLogger('snapperdb.snpdb.export_json')
logger.info('Inititialising SnpDB Class')
#initalise snpdb class
snpdb = SNPdb(config_dict)
#parse confif
snpdb.parse_config_dict(config_dict)
#read strainlist
strain_list = read_file(args.strain_list)
#connect to postgresdb
snpdb._connect_to_snpdb()
#get reference genome path
ref_seq_file = os.path.join(snapperdb.__ref_genome_dir__,
snpdb.reference_genome + '.fa')
#read the reference fasta
ref_seq = read_multi_contig_fasta(ref_seq_file)
snpdb.parse_args_for_export(args, strain_list, ref_seq)
def make_snpdb(config_dict):
snpdb = SNPdb(config_dict)
snpdb._connect_to_snpdb()
snpdb.make_snpdb()
def update_clusters(config_dict):
snpdb = SNPdb(config_dict)
snpdb.parse_config_dict(config_dict)
snpdb._connect_to_snpdb()
snpdb.update_clusters()
def update_distance_matrix(config_dict, args):
logger = logging.getLogger('snapperdb.snpdb.update_distance_matrix')
logger.info('Inititialising SnpDB Class')
snpdb = SNPdb(config_dict)
snpdb.parse_config_dict(config_dict)
snpdb._connect_to_snpdb()
logger.info('Getting strains')
strain_list, update_strain, all_strains = snpdb.get_strains()
# # get_all_good_ids from snpdb2 takes a snp cutoff as well, here, we don't have a SNP cutoff so we set it arbitrarily high.
snp_co = '1000000'
if all_strains or len(update_strain) > 1:
if update_strain:
print "### Populating distance matrix: " + str(
datetime.datetime.now())
snpdb.parse_args_for_update_matrix(snp_co, strain_list)
if args.hpc == 'N':
print '### Launching serial update_distance_matrix ' + str(
datetime.datetime.now())
snpdb.check_matrix(strain_list, update_strain)
snpdb.update_clusters()
else:
print '### Launching parallel update_distance_matrix ' + str(
datetime.datetime.now())
present_stains = list(set(strain_list) - set(update_strain))
for idx, one_strain in enumerate(
chunks(list(update_strain), int(args.hpc))):
snpdb.write_qsubs_to_check_matrix(
args, idx, one_strain, present_stains,
config_dict['snpdb_name'])
snpdb.check_matrix(update_strain, update_strain)
else:
print '### Nothing to update ' + str(datetime.datetime.now())
else:
print '### Nothing to update ' + str(datetime.datetime.now())
def import_json(args):
#set up logging
logger = logging.getLogger('snapperdb.snpdb.import_json')
json_path = untar_file(args.json_file)
json_dict = {}
#import json
try:
with open(json_path) as json_data:
json_dict = json.load(json_data)
json_data.close()
except IOError:
print "Issue with JSON file. Exiting."
exit()
#parse config
args.config_file = json_dict['config_file']
config_dict = parse_config(args)
#initalise snpdb class
snpdb = SNPdb(config_dict)
#parse confif
snpdb.parse_config_dict(config_dict)
#get reference genome path
ref_seq_file = os.path.join(snapperdb.__ref_genome_dir__,
snpdb.reference_genome + '.fa')
#read the reference fasta
ref_seq = read_multi_contig_fasta(ref_seq_file)
#create VCF class
vcf = Vcf()
vcf.parse_json_dict(json_dict, ref_seq)
vcf.depth_average = json_dict['strain_stats']
vcf.sample_name = json_dict['sample']
logger.info('Uploading to SNPdb')
#upload vcf
#connect to snpdb postgres
snpdb._connect_to_snpdb()
snpdb.snpdb_conn = psycopg2.connect(snpdb.conn_string)
if args.write_flag == 'W':
snpdb.snpdb_upload(vcf, args)
#annotate vars
logger.info('Annotating new variants')
snpdb.snpdb_annotate_vars(vcf)
elif args.write_flag == 'R':
print "under development"
def get_the_snps(args, config_dict):
#set up logging
logger = logging.getLogger('snapperdb.snpdb.get_the_snps')
logger.info('Inititialising SnpDB Class')
#initalise snpdb class
snpdb = SNPdb(config_dict)
#parse confif
snpdb.parse_config_dict(config_dict)
#read strainlist
strain_list = read_file(args.strain_list)
#connect to postgresdb
snpdb._connect_to_snpdb()
#get reference genome path
ref_seq_file = os.path.join(snapperdb.__ref_genome_dir__,
snpdb.reference_genome + '.fa')
#read the reference fasta
ref_seq = read_multi_contig_fasta(ref_seq_file)
#add reference genome to strain_list
strain_list.append(snpdb.reference_genome)
#if recombination flag set
rec_dict = {}
if args.rec_file != 'N':
logger.info('Reading recombination list')
rec_dict = read_rec_file_mc(args.rec_file, rec_dict)
if args.gubbins_rec_file != None:
logger.info('Reading gubbins recombination list')
rec_dict = read_rec_file_mc_gubbins(args.gubbins_rec_file, ref_seq,
rec_dict)
#query snadb
snpdb.parse_args_for_get_the_snps_mc(args, strain_list, ref_seq,
snpdb.reference_genome)
snpdb.print_fasta_mc(args, rec_dict)
#print matrix
if args.mat_flag == 'Y':
snpdb.print_matrix(args.out)
# print variant list
if args.var_flag == 'Y':
logger.info('Printing variants')
snpdb.print_vars_mc(args, rec_dict)
def vcf_to_db(args, config_dict, vcf):
#set up loggging
logger = logging.getLogger('snapperdb.snpdb.vcf_to_db')
logger.info('Initialising SNPdb class')
#create snpdb class
snpdb = SNPdb(config_dict)
#parse config into snpdb object
logger.info('Parsing config dict')
snpdb.parse_config_dict(config_dict)
#connect to snpdb postgres
snpdb._connect_to_snpdb()
snpdb.snpdb_conn = psycopg2.connect(snpdb.conn_string)
#check stack?
if inspect.stack()[0][3] == 'fastq_to_db':
# fastq_to_db we will alread have a vcf object to work wih
logger.info('You are running fastq_to_db.')
elif inspect.stack()[0][3] == 'vcf_to_db':
## there is no existing vcf class here, but there will definitely be a vcf
logger.info('You are running vcf_to_db. Initialising Vcf class.')
vcf = Vcf()
logger.info('Making SNPdb variables and output files')
#set up variables
snpdb.define_class_variables_and_make_output_files(args, vcf)
#read vcf
vcf.read_multi_contig_vcf()
logger.info('Uploading to SNPdb')
#upload vcf
snpdb.snpdb_upload(vcf, args)
#annotate vars
logger.info('Annotating new variants')
snpdb.snpdb_annotate_vars(vcf)